The DOI matches its Crossref record; 265 of the 273 references it cites were found in a registry or an index; 3 of the 95 sources its claims cite were checked for support.
Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Open accessAbstract
Data‐driven epoch, the development of machine learning (ML) in materials and device design is an irreversible trend. Its ability and efficiency to handle nonlinear and game‐playing problems is unmatched by traditional simulation computing software and trial‐error experiments. Perovskite solar cells are complex physicochemical devices (systems) that consist of perovskite materials, transport layer materials, and electrodes. Predicting the physicochemical properties and screening the component materials related to perovskite solar cells is the strong point of ML. However, the applications of ML in perovskite solar cells and component materials has only begun to boom in the last two years, so it is necessary to provide a review of the involved ML technologies, the application status, the facing urgent challenges and the development blueprint.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
66 Own interpretation · 58 Sources not found · 31 No basis given · 2 Partly supported · 2 No full text of the sources · 2 Own result · 1 Supported · 1 Own method · accuracy not yet measured
In the paper
“it was found that the structural category descriptors contributed the most to the ML model performance”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.2.2. Transformation of Molecular Descriptors
What the sources say
- Not matchedcites [108] 108. W. Sun, Y. Zheng, K. Yang, Q. Zhang, A. A. Shah, Z. Wu, Y. Sun, L. Feng, D. Chen, Z. Xiao, S. Lu, Y. Li, K. Sun, Sci. Adv. 2019, 5, eaay4275.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirectn = 566- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“Liu et al. [76] built a bandgap database of ABX 3 perovskites and performed bandgap prediction, which further reduced the generalization error of the ML models by refining the types of ABX 3 perovskite”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Data Sources
What the sources say
- Not matchedcites [76] 76. J. Kim, K. Min, Adv. Theory Simul. 2022, 5, 2200068.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“During the training process, the performance of the ML model was greatly improved by increasing the percentage of double perovskite halide data in the training set.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Data Sources
What the sources say
- Not matchedcites [77] 77. A. Jain, G. Hautier, S. P. Ong, K. Persson, J. Mater. Res. 2016, 31, 977.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“found that the effective atomic radius and the number of lone pairs of A-site cations had the greatest effect on the stability of the perovskite phase.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.3. Selection of Suitability Algorithms
What the sources say
- Not matchedcites [136] 136. M. Martinez-Arroyo, L. E. Sucar, 18th Int. Conf. on Pattern Recogni- tion (ICPR'06) 2006, 3, 1236.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“Through the comparison of algorithms, it can be seen that the performance of ensemble learning algorithms is better than that of DL.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.3. Selection of Suitability Algorithms
What the sources say
- Not matchedcites [136] 136. M. Martinez-Arroyo, L. E. Sucar, 18th Int. Conf. on Pattern Recogni- tion (ICPR'06) 2006, 3, 1236.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Therefore, numerous scholars have tried to incorporate ML in their research process that uses these material properties as training data of ML models to perform target prediction.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.1. The Combination of ML and Perovskite Materials
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As shown in Table 7, the application time of ML in PSCs is significantly later than that in perovskite materials.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.2. The Combination of ML and PSCs
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“second the labels of the studies are very similar, mostly bandgap, formability, stability and PCE.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The data type of most PSCCM studies is tabular data, with very few images and text data.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Table 7, most studies did not perform model interpretation or only simply performed feature importance analysis, especially when DL was used to build the models.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 85
- It may state two separate claims as one. 46
- The quote only partly states this claim. 1
Every source the paper cites for this claim was checked and supports it.
In the paper
“The test set cannot replace the validation set to perform hyperparameter search because this would lead to overfitting and make the test set lose its ability to evaluate the generalization capability of the model.[148]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Algorithm
What the sources say
- Supportedcites [148] 148. P. BURMAN, Biometrika 1989, 76, 503. source
Section 4.4.1 says validation data are used to select hyperparameters, while test data are left out to assess the final optimized model. It warns that using data for both model development and assessment can cause overfitting and overestimate model accuracy. Section 4.4.2 likewise says the test-set error is a good estimate because the test data were not used in optimization. (LLM, conf=0.93)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Where interpretation and explanations are not uniformly defined and distinguished.[163,164]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5. Model Interpretation
What the sources say
- Supportedcites [163] 163. L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, L. Kagal, IEEE 5th Int. Conf. on Data Science and Advanced Analytics 2018, 80. https://doi.org/10.1109/dsaa.2018.00018. source
The Introduction (p. 1) says interpretability and explainability have been used interchangeably and argues that they should be distinguished. The Interpretability section (p. 5) also notes the lack of a clear existing definition of interpretability. (LLM, conf=0.93)
- Not matchedcites [164] 164. J.-X. Mi, A.-D. Li, L.-F. Zhou, IEEE Access 2020, 8, 191969.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“They have a similar characteristic that the inner operating mechanism of the models is intuitive, understandable and transparent.[170,171]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.1. Interpretable Algorithmic Model
What the sources say
- Partly supportedcites [170] 170. S. Zheng, C. Ding, Pattern Recogn. Lett. 2020, 131, 227. sourceExperimental — accuracy not yet measured
In “Decision Theory Meets Explainable AI” (Background > Decision Theory; page unavailable), the paper says linear models make importance and utility easy to define, and its Explainable Artificial Intelligence background describes rules as an interpretable form of knowledge. It does not identify GLMs, decision trees, and symbolic regression as a group or say that all three have intuitive, transparent inner mechanisms; it also notes explainability challenges for rule-based systems. (LLM, conf=0.82)
- Not matchedcites [171] 171. Y. Wang, N. Wagner, J. M. Rondinelli, MRS Commun. 2019, 9, 793.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the applications of ML in perovskite solar cells and component materials has only begun to boom in the last two years”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“the power conversion efficiency (PCE) has improved to 25.7% in <10 years.[31]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Abstract
What the sources say
- Not matchedcites [31] 31. D.-K. Lee, N.-G. Park, Sol. RRL 2021, 6, 2100455.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Among them, the average accuracy of the model is lower when SMILES strings are used as input, while the average accuracy of the model is higher when fingerprints of length >1000 bits are used as input.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.2.2. Transformation of Molecular Descriptors
What the sources say
- Not matchedcites [109] 109. A. Mauri, V. Consonni, M. Pavan, R. Todeschini, MATCH Commun. Math. Comput. Chem. 2006, 56, 237.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Third, data collection and regularization are difficult.In our experience, data collection and regularization are not only extremely heavy, accounting for >70% of the entire model building process, but also require the collectors to have extensive knowledge and background related to PSCCM.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“At this stage, the physicochemical and structural data of perovskite materials, the component and performance test data of device-level PSCs are still in a barren and isolated state.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.1. Model Improvement Stage
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“First, the articles that combine ML with PSCCM exploded in the last two years”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Predicting the physicochemical properties and screening the component materials related to perovskite solar cells is the strong point of ML.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Its ability and efficiency to handle nonlinear and game-playing problems is unmatched by traditional simulation computing software and trial-error experiments.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“when the order of magnitude differences of data are too large, data normalization can be used, which can increase the efficiency and accuracy of the model.[75]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Data Sources
What the sources say
- Not matchedcites [75] 75. Y. Liu, W. Yan, H. Zhu, Y. Tu, L. Guan, X. Tan, Org. Electron. 2022, 101, 106426.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“As of today, ML has also been deeply wedged into the materials field.[14][15][16][17][18][19][20][21]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Abstract
What the sources say
- Not matchedcites [14] 14. G. R. Schleder, A. C. M. Padilha, C. M. Acosta, M. Costa, A. Fazzio, J. Phys.: Mater. 2019, 2, 032001.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [15] 15. G. H. Gu, J. Noh, I. Kim, Y. Jung, J. Mater. Chem. A 2019, 7, 17096.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [16] 16. G. L. W. Hart, T. Mueller, C. Toher, S. Curtarolo, Nat. Rev. Mater. 2021, 6, 730.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The computational cost of ML is much lower than that of contemporaneous experimental and computational methods.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“ML can predict the properties of unknown PSCs from existing data of PSCs, which can accelerate the design and development of PSCs.[55]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 1. Introduction
What the sources say
- Not matchedcites [55] 55. W. Xu, Z. Liu, R. T. Piper, J. W. P. Hsu, Sol. Energy Mater. Sol. Cells 2023, 249, 112055.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“ML, the greatest product of the data-driven epoch, accelerates the process of developing new materials and even bring inspirations that are beyond the reach of human hands and new research perspectives to materials scientists.[26][27][28][29]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Abstract
What the sources say
- Not matchedcites [26] 26. Y. Juan, Y. Dai, Y. Yang, J. Zhang, J. Mater. Sci. Technol. 2021, 79, 178.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [27] 27. P. Raccuglia, K. C. Elbert, P. D. F. Adler, C. Falk, M. B. Wenny, A. Mollo, M. Zeller, S. A. Friedler, J. Schrier, A. J. Norquist, Nature 2016, 533, 73.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [28] 28. C. Gao, X. Min, M. Fang, T. Tao, X. Zheng, Y. Liu, X. Wu, Z. Huang, Adv. Funct. Mater. 2021, 32, 2108044.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Most of the data in the field of PSCCM are tabular data.There are usually no obvious space or time links between features, and the sample volume of the data is small and heterogeneous, which leads to ensemble learning algorithms with trees that typically outperform DL. [122,123]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.3. Selection of Suitability Algorithms
What the sources say
- Not matchedcites [122] 122. R. Shwartz-Ziv, A. Armon, Inform. Fusion 2022, 81, 84.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [123] 123. O. Sagi, L. Rokach, WIREs Data Mining Knowl. Discov. 2018, 8, e1249.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“DL belongs to a subset of ML that originated from artificial neural networks and excels to process unstructured data (images, text and sound).[128,129]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.3. Selection of Suitability Algorithms
What the sources say
- Not matchedcites [128] 128. R. Miotto, F. Wang, S. Wang, X. Jiang, J. T. Dudley, Briefings in Bioinformatics 2018, 19, 1236.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [129] 129. A. Shrestha, A. Mahmood, IEEE Access 2019, 7, 53040.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“An appropriate amount of experimental and computational data on perovskite solar cells and component materials (PSCCM) already exists, and the related ML technologies are mature.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This illustrates that the ML can be applied only when a large amounts of PSCs related data exists, and further supports the importance of the data for the ML models.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.1. Data Sources and Regularization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This may be due to the fact that the SMILES string is still close to the original information of the data that causes some ML algorithms not to extract hidden features, while longer fingerprints can contain more chemical information making them more unique and easily available for model training.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.2.2. Transformation of Molecular Descriptors
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“The proper extraction of features largely affects the simplicity and generalization capability of the model and the effectiveness of the model interpretation.[78][79][80]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.2.1. Feature Extraction
What the sources say
- Not matchedcites [78] 78. V. Bolón-Canedo, A. Alonso-Betanzos, Inform. Fusion 2019, 52, 1.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [79] 79. P. M. Khan, K. Roy, Expert Opinion on Drug Discovery 2018, 13, 1075.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [80] 80. Y. Li, T. Li, H. Liu, Knowledge and Information Systems 2017, 53, 551.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“The SMILES string and the chemical structure of the molecule are in unique correspondence that can be used as a fingerprint of the chemical structure to express differences of chemical structure, elemental and linkage properties, etc. [107]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.2.2. Transformation of Molecular Descriptors
What the sources say
- Not matchedcites [107] 107. Z.-W. Zhao, M. del Cueto, Y. Geng, A. Troisi, Chem. Mater. 2020, 32, 7777.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“It is no exaggeration that data is the life of the ML models, and even data is more important than the algorithm itself.[57]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.1. Data Sources and Regularization
What the sources say
- Not matchedcites [57] 57. C. Chen, Y. Zuo, W. Ye, X. Li, Z. Deng, S. P. Ong, A Adv. Energy Mater. 2020, 10, 1903242.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For the above-mentioned complicated influencing factors, it is difficult to conduct a comprehensive analysis by a low-cost experimental or computational method.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“It avoids the shortcomings inherent in a single model or a model with a single set of parameters, and the final optimized prediction model has strong robustness and anti-overfitting capability.[124,125]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.3. Selection of Suitability Algorithms
What the sources say
- Not matchedcites [124] 124. X. Dong, Z. Yu, W. Cao, Y. Shi, Q. Ma, Front. Computer Sci. 2019, 14, 241.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [125] 125. Y. Ren, L. Zhang, P. N. Suganthan, IEEE Comput. Intell. Mag. 2016, 11, 41.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“ML is subtly replacing traditional techniques in numerous fields, such as, biopharmaceuticals, [11] image identification, [12] data mining, [13] etc.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Abstract
What the sources say
- Not matchedcites [11] 11. Y. Yang, R. Zhang, Z. Li, L. Mei, S. Wan, H. Ding, Z. Chen, J. Xing, H. Feng, J. Han, H. Jiang, M. Zheng, C. Luo, B. Zhou, J. Med. Chem. 2020, 63, 1337.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [12] 12. A. Esteva, K. Chou, S. Yeung, N. Naik, A. Madani, A. Mottaghi, Y. Liu, E. Topol, J. Dean, R. Socher, npj Digital Med. 2021, 4, 5.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [13] 13. E. A. Olivetti, J. M. Cole, E. Kim, O. Kononova, G. Ceder, T. Y.-J. Han, A. M. Hiszpanski, Appl. Phys. Rev. 2020, 7, 041317.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“The dimensionality reduction of features can enhance the robustness of the model, reduce the computational cost, and remove the noise in the dataset, but this may lose some important information and obscure the meaning of the features, which are mostly used in processing image-type data.[89,90]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.2.1. Feature Extraction
What the sources say
- Not matchedcites [89] 89. M. Lualdi, M. Fasano, J. Proteomics 2019, 198, 18.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [90] 90. T. Zhou, Z. Song, K. Sundmacher, Engineering 2019, 5, 1017.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Molecular fingerprint is now the most commonly used technique for the transformation of molecular descriptors, because it maximally preserves the structural and physicochemical information of the molecule as an input to the ML models.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.2.2. Transformation of Molecular Descriptors
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“It is the third-generation solar cell with the most promising commercialization, [30]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Abstract
What the sources say
- Not matchedcites [30] 30. X. Luo, X. Lin, F. Gao, Y. Zhao, X. Li, L. Zhan, Z. Qiu, J. Wang, C. Chen, L. Meng, X. Gao, Y. Zhang, Z. Huang, R. Fan, H. Liu, Y. Chen, X. Ren, J. Tang, C.-H. Chen, D. Yang, Sci. China: Chem. 2022, 65, 2369.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The choice of suitability algorithm is usually judged by the credibility level on the model, the type (table, image, text, etc.) and volume of data.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.3. Selection of Suitability Algorithms
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Therefore, the intervention of ML for the study of PSCs may be another promising direction.[53,54]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 1. Introduction
What the sources say
- Not matchedcites [53] 53. Z. Wang, M. Yang, X. Xie, C. Yu, Q. Jiang, M. Huang, H. Algadi, Z. Guo, H. Zhang, Adv. Composites Hybrid Mater. 2022, 5, 2700.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [54] 54. W. Yan, Y. Liu, Y. Zang, J. Cheng, Y. Wang, L. Chu, X. Tan, L. Liu, P. Zhou, W. Li, Z. Zhong, Nano Energy 2022, 99, 107394.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Not matched
In the paper
“LIME provides a local explanation for the black box model, that is, it explains the prediction results of a single sample.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
What the sources say
- Not matchedcites [176] 176. S. Lundberg, S.-I. Lee, Adv. Neural Inform. Processing Syst. 2017, 30, 4765.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The performance of the PSCs is influenced by multiple scales and multiple domains of physicochemical features, such as the properties of the material itself, the compatibility between materials, and the structure and fabrication of the PSCs”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.2. The Combination of ML and PSCs
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At this point, the traditional DFT calculation and simulation methods are no longer applicable in terms of efficiency and performance, whereas ML is the best solution nowadays.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.2. The Combination of ML and PSCs
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The concepts related to model validation and optimization are easily confused, and the application of these techniques is not standardized”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Algorithm
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Optimization of hyperparameters is essential because hyperparameters under the same algorithm can directly affect the performance of the model, which can lead to underfitting or overfitting of the model.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Algorithm
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“SHAP has developed several different explainers for black box models.Among them, TreeExplainer and DeepExplainer are used for ensemble learning and DL respectively.[122,178]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
What the sources say
- Not matchedcites [122] 122. R. Shwartz-Ziv, A. Armon, Inform. Fusion 2022, 81, 84.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [178] 178. J. Klaise, A. Looveren, G. Vacanti, J. Mach. Learn. Res. 2021, 22, 8194.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“Among them, the FA (formamidinium) content plays the most important contribution to the predicted value of PCE, and the high FA content contributes more positively to PCE than the negative contribution when the FA content is low.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
What the sources say
- Not matchedcites [205] 205. A. Mannodi-Kanakkithodi, M. K. Y. Chan, J. Mater. Sci. 2022, 57, 10736.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“In this sample, the model prediction for PCE is 23.53%, and the SHAP explanation shows that the high prediction value is mainly attributed to the high FA content, high hole mobility and suitable bandgap.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Name Description Path
What the sources say
- Not matchedcites [205] 205. A. Mannodi-Kanakkithodi, M. K. Y. Chan, J. Mater. Sci. 2022, 57, 10736.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For PSC, the role of model interpretation may be more important than model performance, because it can give materials scientists some guidance or inspiration to explore the feature connections and potential causal laws behind complex physicochemical systems.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5. Model Interpretation
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of course, ML can greatly improve the efficiency of material screening and reduce the computational burden of DFT, which demonstrates the great potential of combining ML and DFT in the field of perovskite materials.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.1. The Combination of ML and Perovskite Materials
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“LIME can be used for the explanation of any black box model and input data, but it also has some drawbacks.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
What the sources say
- Not matchedcites [176] 176. S. Lundberg, S.-I. Lee, Adv. Neural Inform. Processing Syst. 2017, 30, 4765.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“This has a smaller error and more representative of the total sample.[153]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Algorithm
What the sources say
- Not matchedcites [153] 153. D. Krstajic, L. J. Buturovic, D. E. Leahy, S. Thomas, J. Cheminform. 2014, 6, 10.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Performing repeated operations may result in different explanations due to the differences of generated new samples.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The performance of ensemble learning and DL is usually better than classical learning, but their internal working mechanisms also become more complex (e.g., DL uses many nonlinear overlays to improve the performance of the model). [157]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5. Model Interpretation
What the sources say
- Not matchedcites [157] 157. P. P. Angelov, E. A. Soares, R. Jiang, N. I. Arnold, P. M. Atkinson, WIREs Data Mining Knowl. Discov. 2021, 11, e1424.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It only splits the data into training set and test set without setting the validation set, and only performs model training and testing once, so it does not have model optimization ability.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Algorithm
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, it ignores the relationship between features in the generation of new samples, which may lead to the generation of some impossible samples.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, these traditional methods are either less accurate or require a lot of computational resources and time.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.1. The Combination of ML and Perovskite Materials
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The interpretable algorithmic models generally belong to the global interpretation type, namely, they are able to interpret the entire model's operating mechanism based on the complete data set.[167]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5. Model Interpretation
What the sources say
- Not checked — no full text of the sourcecites [167] 167. M. R. Zafar, N. M. Khan, arXiv preprint arXiv:1906.10263, 2019. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The field of PSCCM commonly uses ML to deal with classification and regression problems”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.3. Selection of Suitability Algorithms
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The 67 papers with detailed ML technical descriptions and representation are included in Table 7 and deconstructed in 10 dimensions”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3. Applications of ML in the PSCCM Field
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“But it also leads to the application of ML models in perovskite materials mostly as high-throughput virtual materials screening, which greatly reduces the credibility and practicability of ML models.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.1. The Combination of ML and Perovskite Materials
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“The theory of SHAP explanation is to calculate the cumulative contribution of the features to the predicted values.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
What the sources say
- Not matchedcites [177] 177. H. Chen, S. Lundberg, S.-I. Lee, in Explainable AI in Healthcare and Medicine, (Eds: A. Shaban-Nejad, M. Michalowski, D. L. Buck- eridge), Springer, Cham, Switzerland 2020, 914, 261.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“After that, they used SHAP to explain the model PCE RF .”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
What the sources say
- Not matchedcites [205] 205. A. Mannodi-Kanakkithodi, M. K. Y. Chan, J. Mater. Sci. 2022, 57, 10736.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared the screening of virtual perovskite materials by ML model, the parameters of PSCs are easily verified experimentally, which increases the credibility of ML model for application in PSCs.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.2. The Combination of ML and PSCs
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“As shown in Figure 9a, SHAP visualizes the importance ranking of the 13 features and the contribution of each feature.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
What the sources say
- Not matchedcites [205] 205. A. Mannodi-Kanakkithodi, M. K. Y. Chan, J. Mater. Sci. 2022, 57, 10736.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“SR is a representative interpretable ML algorithm that attempts to create optimal mathematical formulations to map the connection between features and predictive targets.[172,173]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.1. Interpretable Algorithmic Model
What the sources say
- Not matchedcites [172] 172. D. A. Augusto, H. J. C. Barbosa, Proc. Sixth Brazilian Symposium on Neural Networks 2000, 1, 173.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [173] 173. G. Peake, J. Wang, Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining 2018, 19.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The interpretation of the model is used to determine whether the model captures valuable features and thus judge whether the model can be transferred to the prediction of other samples, which is significant for small sample databases.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5. Model Interpretation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There are two main sources of data related to PSC, one is manual experiments or literature collection, and the other is generation by simulation software.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.2. The Combination of ML and PSCs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thanks to the massive accumulation of data related to PSCCM, there is space for ML to cut in.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3. Applications of ML in the PSCCM Field
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“SHAP is the most popular explanation method for black box models nowadays, which originated from game theory and was first applied to local and global explanation of black box models in 2017.[177]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5.2. External Explanations of Black-Box Model
What the sources say
- Not matchedcites [177] 177. H. Chen, S. Lundberg, S.-I. Lee, in Explainable AI in Healthcare and Medicine, (Eds: A. Shaban-Nejad, M. Michalowski, D. L. Buck- eridge), Springer, Cham, Switzerland 2020, 914, 261.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“The operational mechanism of the”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5. Model Interpretation
What the sources say
- Not matchedcites [160] 160. M. Sahakyan, Z. Aung, T. Rahwan, IEEE Access 2021, 9, 135392.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Rests on an assumption: Model performance is only the result of model decisions, and the reasons for model decisions are sometimes not clear to us.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the operating mechanism of the model is difficult to understand, it will lead to credibility problems, especially the screening of virtual perovskite materials and the design of new materials.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.5. Model Interpretation
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“In contrast to traditional ML algorithms, it can automatically extract efficient features from unstructured data and train them.[130,131]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 2.3. Selection of Suitability Algorithms
What the sources say
- Not matchedcites [130] 130. S. So, T. Badloe, J. Noh, J. Bravo-Abad, J. Rho, Nanophotonics 2020, 9, 1041.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [131] 131. G. P. Zhang, IEEE Trans. Systems, Man Cybernetics, Part c 2000, 30, 451.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The features of these materials in turn involve thousands of properties such as physical, electronic, structural, morphological, and chemical etc.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“4) The vague model training process, mainly shown that the lack of evaluation of model underfitting and overfitting.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ML models composed of ensembles learning or DL algorithms generally have better performance for PSCCM, but these complex models are less interpretable, which makes them less credible.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These functional relations may be very important because it may reveal potential physicochemical relations between microscopic features and the attributes of macroscopic target.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.3. ML Inspiration Stage
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“But this problem can be avoided using ML replacement, because the data comes from multiple experimental data and the model will analyze and compare to make the most representative judgment.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.2. ML Replacement Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The accuracy of the ML model was 82% higher than 70% when using the ideal factor.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.3. ML Applications of PSCCM under Data Barrenness
What the sources say
- Not matchedcites [228] 228. A. Talapatra, B. P. Uberuaga, C. R. Stanek, G. Pilania, Chem. Mater. 2021, 33, 845.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although there are methods that can help to select the number of inputs (see Section 2.2.1), these methods also have limitations, such as not taking into account the potential physicochemical links between features and not being able to select the types of features.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“5) The vague hyperparameter search range.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“1) Overly sparse and unbalanced distribution of training data.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In PSCs, ML is mainly used for feature analysis to find the key features that affect the cell properties and to accelerate and guide the experimental process.Of course, it requires complete feature construction logic and high interpretability of the model.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Step 6: Experimental validation:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“2) The generalization capability of the model is poor.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“DFT is an important method of generating computational data for perovskite materials, but its computational cost for high-precision data is too high.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.2. ML Replacement Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There exist various ML algorithms that can the generate functional relations between features and target attributes only from data.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.3. ML Inspiration Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“1) The difficulty of obtaining the inputs is not less than the difficulty of obtaining the labels of the model.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PSCs are one of the complex physicochemical spaces composed of perovskite materials, the study of ML application in PSCs has important reference value and transfer possibility for other spaces (LED, photodetectors, etc.).”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.2. The Combination of ML and PSCs
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The supplementation of PSCCM data is a long-term project, and it is likely that some of the data are already inadequate (e.g., the information of tandem or large-area PSCs, information of non-toxic perovskite materials, etc.).”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.1. Model Improvement Stage
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Bayesian optimization is an active learning algorithm that allows iterative optimization of the model based on small samples, thus reducing the reliance on high-cost experimental data of PSCCM.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.1. Model Improvement Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The molecular descriptors generated by the currently used feature transformation tools (see Section 2.2.2) do not represent the full properties of the material, and cannot take into account the requirements of accuracy, speed, and interpretability.[255]”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
What the sources say
- Not matchedcites [255] 255. A. Hernandez, A. Balasubramanian, F. Yuan, S. A. M. Mason, T. Mueller, npj Comput. Mater. 2019, 5, 112.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“4) The predicted target properties can be expressed by a simple function mapping.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, these characterization tests usually require the fabrication of complete devices and the devices used in different characterizations are not the same, which results in a waste of experimental resources.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.2. ML Replacement Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“7) The shortage and homogeneity of model interpretation.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“6) Unreasonable evaluation method for model generalization capability, mainly shown that the unreasonable use of training set, validation set and test set.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“3) Unreasonable splitting of training and test sets.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“As shown in Figure 15e, the recombination processes in PSCs are classified into three categories (band-to-band, grain boundary traps, and interfacial traps (Front/Back interface)), which cannot be effectively classified when the conventional ideal factor is used to classify them.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.3. ML Applications of PSCCM under Data Barrenness
What the sources say
- Not matchedcites [228] 228. A. Talapatra, B. P. Uberuaga, C. R. Stanek, G. Pilania, Chem. Mater. 2021, 33, 845.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“PSCs are complex physicochemical systems composed of a variety of materials, including perovskite materials, organic small molecule materials, polymer materials, metal oxides and many other materials with different properties.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which requires the researcher to have knowledge of both PSCCM and ML fields.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast to traditional experimental methods, the autonomous robotic experimental flow allows for uninterrupted and long experiments without human involvement, and analyze data via ML to optimize the experiments, which brings the possibility of efficient and costeffective experiments.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.2. ML Replacement Stage
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There is no essential difference between meta-learning and transfer learning.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.1. Model Improvement Stage
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Specifically, the PCE, Voc, Jsc, and FF are closely related for the same PSC, so the knowledge learned in the PCE prediction model can be transferred to the prediction models of Voc, Jsc, and FF.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.1. Model Improvement Stage
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The device-level characterization of PSCs includes a variety of tests, such as electrical performance test, External quantum efficiency (EQE) test, EIS test and stability test.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.2. ML Replacement Stage
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In perovskite materials, the applications of ML are tilted toward the screening of virtual materials, which requires the model to have high generalization capability, and the interpretation and feature analysis of the model are less demanding.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Step 6: Experimental validation:
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is the deconstruction and reconstruction of known materials, which in turn generates new materials that better meet the target requirements.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.3. ML Inspiration Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, for the electrical performance characterization of PSCs with popular components and structures, the test results may not be the same or even test errors may occur due to small differences in each experimenter's skill level and characterization equipment.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.2. ML Replacement Stage
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“3) The fitting of the model requires a large amount of data.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, it is possible to transfer the inputs used under the same label for similar perovskite materials.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.1. Model Improvement Stage
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Adequate and abundant data of PSCCM is a guarantee for the application of ML into the following two stages.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.1. Model Improvement Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Device-level characterization tests are essentially a mapping between device performance and the properties of the material used, which is close to the concept of ML.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.2. ML Replacement Stage
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to the short time of combination between ML and PSCCM, many study steps are incomplete or even unreasonable.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: The combination between ML and PSCCM has been short.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unlike high-throughput material screening via ML, ML generation of new component materials of PSC is more purposeful and efficient.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.3. ML Inspiration Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In other words, the functional relations between the performance of PSCs and the properties of the component materials are used to help people fully understand the performance of PSCs, and to screen and design new component materials that meet the requirements.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.3. ML Inspiration Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the target properties are classified more elaborately, the performance of models of that kind will be better, but when the models are used for different kinds of targets, the performance will be greatly reduced.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“2) Overly redundant algorithm comparisons.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 4. Urgent Challenges
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For PSC, ML can generate new passivation/interface materials composed of organic small molecules based on lewis acid-base interactions, and additive materials that improve the growth of perovskite crystals can also be generated based on structural information such as functional groups and chain length.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 5.3. ML Inspiration Stage
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper relies on this without support.
PSC databases are small sample size datasets.
“suitable for small sample size datasets (e.g., PSC databases)”
The paper relies on this without support.
Hold-out (Model Optimization) is rarely used in small sample data sets.
“Hold-out (Model Optimization) is rarely used in small sample data sets.”
The paper relies on this without support.
Model performance is only the result of model decisions, and the reasons for model decisions are sometimes not clear to us.
“model performance is only the result of model decisions, and the reasons for model decisions are sometimes not clear to us.”
The paper relies on this without support.
Interpretable models perform poorly in handling complex classification and regression problems, especially when there are interactions between features of the data.
“Although these models are easy to understand, they perform poorly in handling complex classification and regression problems, especially when there are interactions between features of the data.”
The paper relies on this without support.
The combination between ML and PSCCM has been short.
“Due to the short time of combination between ML and PSCCM, many study steps are incomplete or even unreasonable.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“0.990/16.741/11.737K”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3. Applications of ML in the PSCCM Field
Reported 16.741 · recomputed 137.757169
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Algorithm
“F1-Score is the harmonic mean of Precision and Accuracy.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Algorithm
“F1 Score 1 2 2 F TP TP FP FN = + +”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > Data Sources
“Kim et al. [77] used the Light GBM algorithm model to predict the properties (formation energy, convex hull energy, bandgap) of A 2 BB'X 3 double perovskite.”
§ Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects > 3.1. The Combination of ML and Perovskite Materials
“double perovskite structure (A 2 BB'X 6 , AA'BB'X 6 )”
Citations
Cites
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 76,871
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,925
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 7,005
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,657
PubChem Substance and Compound databases
Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
Generative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540
The Handbook of brain theory and neural networks
(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,913
Selection of relevant features and examples in machine learning
Blum, A. L., & Pat Langley, B. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00063-5 · Cited by 3,294
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107
Sagi, O., & Rokach, L. (2018) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1249 · Cited by 3,103
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints
Yap, C. W. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21707 · Cited by 2,979
Explainable AI: a review of machine learning interpretability methods
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922
Quinlan, J. R. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80053-6 · Cited by 2,553
Studies in Molecular Dynamics. I. General Method
Alder, B. J., & Wainwright, T. E. (1959) · The Journal of Chemical Physics · doi:10.1063/1.1730376 · Cited by 2,524
Explaining Explanations: An Overview of Interpretability of Machine Learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507
Bias in error estimation when using cross-validation for model selection
Varma, S., & Simon, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-91 · Cited by 2,485
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312
Inverse molecular design using machine learning: Generative models for matter engineering
Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,177
Tabular data: Deep learning is not all you need
Shwartz-Ziv, R., & Armon, A. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.011 · Cited by 2,172
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020) · Frontiers of Computer Science · doi:10.1007/s11704-019-8208-z · Cited by 1,928
Machine learning algorithm validation with a limited sample size
Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0224365 · Cited by 1,901
Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
Rodriguez, J. D., Perez, A., & Lozano, J. A. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.187 · Cited by 1,890
Review of Deep Learning Algorithms and Architectures
Shrestha, A., & Mahmood, A. (2019) · IEEE Access · doi:10.1109/access.2019.2912200 · Cited by 1,883
Machine Learning Interpretability: A Survey on Methods and Metrics
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019) · Electronics · doi:10.3390/electronics8080832 · Cited by 1,842
Crystallography Open Database – an open-access collection of crystal structures
Gražulis, S., Chateigner, D., Downs, R. T., Yokochi, A., Quirós, M., Lutterotti, L., Manakova, E., Butkus, J., Moeck, P., & Bail, A. L. (2009) · Journal of Applied Crystallography · doi:10.1107/s0021889809016690 · Cited by 1,827
Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727
Neural networks for classification: a survey
Zhang, G. (2000) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/5326.897072 · Cited by 1,707
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631
AFLOW: An automatic framework for high-throughput materials discovery
Stefano Curtarolo, A., Setyawan, W., Hart, G. L. W., Jahnátek, M., Chepulskii, R. V., Taylor, R. H., Wang, S., Junkai Xue, A., Yang, K., Levy, O., et al. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.02.005 · Cited by 1,577
The Handbook of brain theory and neural networks
(2003) · Choice Reviews Online · doi:10.5860/choice.41-0300 · Cited by 1,540
Burger, B., Maffettone, P. M., Gusev, V. V., Aitchison, C. M., Bai, Y., Wang, X., Li, X., Alston, B. M., Li, B., Clowes, R., et al. (2020) · Nature · doi:10.1038/s41586-020-2442-2 · Cited by 1,516
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Altman, N. (1992) · The American Statistician · doi:10.2307/2685209 · Cited by 1,420
Deep learning-enabled medical computer vision
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406
Self-citationMaterials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
Self-citationTravis, W., Glover, E. N. K., Bronstein, H. A., Scanlon, D. O., & Palgrave, R. G. (2016) · Chemical Science · doi:10.1039/c5sc04845a · Cited by 1,217
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
Cross-validation pitfalls when selecting and assessing regression and classification models
Krstajic, D., Buturovic, L. J., Leahy, D. E., & Thomas, S. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-10 · Cited by 1,090
Reliable Accuracy Estimates from k-Fold Cross Validation
Wong, T.-T., & Yeh, P.-Y. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2912815 · Cited by 1,074
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983
The inorganic crystal structure data base
Bergerhoff, G., Hundt, R., Sievers, R. B., & Brown, I. D. (1983) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00038a003 · Cited by 945
Self-driving laboratory for accelerated discovery of thin-film materials
MacLeod, B. P., Parlane, F. G. L., Morrissey, T. D., Häse, F., Roch, L. M., Dettelbach, K. E., Moreira, R., Yunker, L. P. E., Rooney, M. B., Deeth, J. R., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz8867 · Cited by 905
DScribe: Library of descriptors for machine learning in materials science
Himanen, L., Jäger, M. O. J., Morooka, E. V., Canova, F. F., Ranawat, Y. S., Gao, D. Z., Rinke, P., & Foster, A. S. (2019) · Computer Physics Communications · doi:10.1016/j.cpc.2019.106949 · Cited by 901
Molecular fingerprint similarity search in virtual screening
Cereto‐Massagué, A., Ojeda, M. J., Valls, C., Mulero, M., Garcı́a-Vallvé, S., & Pujadas, G. (2014) · Methods · doi:10.1016/j.ymeth.2014.08.005 · Cited by 881
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
Explainable artificial intelligence: an analytical review
Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1424 · Cited by 843
Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00831 · Cited by 815
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808
Feature dimensionality reduction: a review
Jia, W., Sun, M., Lian, J., & Hou, S. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00637-x · Cited by 797
Leakage in data mining: Formulation, detection, and avoidance
Kaufman, S., Rosset, S., Perlich, C., & Stitelman, O. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2382577.2382579 · Cited by 791
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
Lu, S., Zhou, Q., Ouyang, Y., Guo, Y., Li, Q., & Wang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05761-w · Cited by 725
Hart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703
Ren, Y., Zhang, L., & Suganthan, P. N. (2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2471235 · Cited by 700
BURMAN, P. (1989) · Biometrika · doi:10.1093/biomet/76.3.503 · Cited by 634
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609
Deep learning enabled inverse design in nanophotonics
So, S., Badloe, T., Noh, J., Rho, J., & Bravo‐Abad, J. (2020) · Nanophotonics · doi:10.1515/nanoph-2019-0474 · Cited by 564
Opportunities and Challenges for Machine Learning in Materials Science
Morgan, D., & Jacobs, R. (2020) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010015 · Cited by 549
Rajan, K., & Mendez, P. F. (2005) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10022 · Cited by 546
Machine learning for perovskite materials design and discovery
Tao, Q., Xu, P., Li, M., & Lu, W. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00495-8 · Cited by 542
Ensembles for feature selection: A review and future trends
Bolón-Canedo, V., & Alonso-Betanzos, A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.008 · Cited by 541
Rajan, K. (2005) · Materials Today · doi:10.1016/s1369-7021(05)71123-8 · Cited by 539
Machine learning bandgaps of double perovskites
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., & Lookman, T. (2016) · Scientific Reports · doi:10.1038/srep19375 · Cited by 518
A survey on data preprocessing for data stream mining: Current status and future directions
Ramírez-Gallego, S., Krawczyk, B., García, S., Woźniak, M., & Herrera, F. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.078 · Cited by 502
Discovering Physical Concepts with Neural Networks
Iten, R., Metger, T., Wilming, H., del Rio, L., & Renner, R. (2020) · Physical Review Letters · doi:10.1103/physrevlett.124.010508 · Cited by 498
Interface and Defect Engineering for Metal Halide Perovskite Optoelectronic Devices
Han, T. H., Tan, S., Xue, J., Meng, L., Lee, J., & Yang, Y. (2019) · Advanced Materials · doi:10.1002/adma.201803515 · Cited by 482
Recent Progress on the Long‐Term Stability of Perovskite Solar Cells
Fu, Q., Tang, X., Huang, B., Hu, T., Tan, L., Chen, L., & Chen, Y. (2018) · Advanced Science · doi:10.1002/advs.201700387 · Cited by 454
Interpretable and Explainable Machine Learning for Materials Science and Chemistry
Oviedo, F., Ferres, J. L., Buonassisi, T., & Butler, K. T. (2022) · Accounts of Materials Research · doi:10.1021/accountsmr.1c00244 · Cited by 435
A historical perspective of explainable Artificial Intelligence
Confalonieri, R., Çoba, L., Wagner, B., & Besold, T. R. (2020) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1391 · Cited by 431
ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation
Dong, J. N., Cao, D., Miao, H., Shao, L., Deng, B., Yun, Y., Wang, N., Lü, A., Zeng, W., & Chen, A. F. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0109-z · Cited by 415
Pushing the frontiers of density functionals by solving the fractional electron problem
Kirkpatrick, J., McMorrow, B., Turban, D. H. P., Gaunt, A. L., Spencer, J. S., Matthews, A., Obika, A., Thiry, L., Fortunato, M., Pfau, D., et al. (2021) · Science · doi:10.1126/science.abj6511 · Cited by 409
Recent advances in feature selection and its applications
Li, Y., Li, T., & Liu, H. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1059-8 · Cited by 400
Zhou, T., Song, Z., & Sundmacher, K. (2019) · Engineering · doi:10.1016/j.eng.2019.02.011 · Cited by 393
Machine learning in concrete science: applications, challenges, and best practices
Li, Z., Yoon, J., Zhang, R., Rajabipour, F., Srubar III, W. V., Dabo, I., & Radlińska, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00810-x · Cited by 388
Machine learning for high performance organic solar cells: current scenario and future prospects
Mahmood, A., & Wang, J.-L. (2021) · Energy & Environmental Science · doi:10.1039/d0ee02838j · Cited by 378
Weng, B., Song, Z., Zhu, R., Yan, Q., Sun, Q., Grice, C. G., Yan, Y., & Yin, W.-J. (2020) · Nature Communications · doi:10.1038/s41467-020-17263-9 · Cited by 376
Jacobsson, T. J., Hultqvist, A., García-Fernández, A., Anand, A., Al-Ashouri, A., Hagfeldt, A., Crovetto, A., Abate, A., Ricciardulli, A. G., Vijayan, A., et al. (2021) · Nature Energy · doi:10.1038/s41560-021-00941-3 · Cited by 372
Potential lead toxicity and leakage issues on lead halide perovskite photovoltaics
Ren, M., Qian, X., Chen, Y., Wang, T., & Zhao, Y. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2021.127848 · Cited by 360
Hybrid feature selection by combining filters and wrappers
Hsu, H.-H., Hsieh, C.-W., & Lu, M.-D. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.12.156 · Cited by 356
Photovoltaic Performance of Perovskite Solar Cells with Different Grain Sizes
Kim, H. D., Ohkita, H., Benten, H., & Ito, S. (2015) · Advanced Materials · doi:10.1002/adma.201504144 · Cited by 355
Energy Level Alignment at Interfaces in Metal Halide Perovskite Solar Cells
Wang, S., Sakurai, T., Wen, W., & Qi, Y. (2018) · Advanced Materials Interfaces · doi:10.1002/admi.201800260 · Cited by 352
Sun, W., Zheng, Y., Yang, K., Zhang, Q., Shah, A. A., Wu, Z., Sun, Y., Feng, L., Chen, D., Xiao, Z., et al. (2019) · Science Advances · doi:10.1126/sciadv.aay4275 · Cited by 344
Schmidt, J., Shi, J., Borlido, P., Chen, L., Botti, S., & Marques, M. A. L. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.7b00156 · Cited by 341
Sun, S., Hartono, N. T. P., Ren, Z., Oviedo, F., Buscemi, A. M., Layurova, M., Chen, D. X., Ogunfunmi, T., Thapa, J., Ramasamy, S., et al. (2019) · Joule · doi:10.1016/j.joule.2019.05.014 · Cited by 336
Oviedo, F., Ren, Z., Sun, S., Settens, C., Liu, Z., Hartono, N. T. P., Ramasamy, S., DeCost, B. L., Tian, S. I. P., Romano, G., et al. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0196-x · Cited by 327
Symbolic regression in materials science
Wang, Y., Wagner, N., & Rondinelli, J. M. (2019) · MRS Communications · doi:10.1557/mrc.2019.85 · Cited by 322
Machine learning for renewable energy materials
Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta02356a · Cited by 311
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Wainer, J., & Cawley, G. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115222 · Cited by 309
Perovskite solar cells based on screen-printed thin films
Chen, C., Chen, J., Han, H., Chao, L., Hu, J., Niu, T., Dong, H., Yang, S., Xia, Y., Chen, Y., et al. (2022) · Nature · doi:10.1038/s41586-022-05346-0 · Cited by 305
Self-citationData-driven materials research enabled by natural language processing and information extraction
Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y.-J., & Hiszpanski, A. M. (2020) · Applied Physics Reviews · doi:10.1063/5.0021106 · Cited by 302
Materials Data Science: Current Status and Future Outlook
Kalidindi, S. R., & De Graef, M. (2015) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070214-020844 · Cited by 294
An overview of molecular fingerprint similarity search in virtual screening
Muegge, I., & Mukherjee, P. K. (2015) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2016.1117070 · Cited by 290
Imputation of Missing Data in Industrial Databases
Lakshminarayan, K., Harp, S., & Samad, T. (1999) · Applied intelligence (Boston) · doi:10.1023/a:1008334909089 · Cited by 290
A critical examination of compound stability predictions from machine-learned formation energies
Bartel, C. J., Trewartha, A., Wang, Q., Dunn, A., Jain, A., & Ceder, G. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00362-y · Cited by 277
Zhai, S., Xie, H., Cui, P., Guan, D., Wang, J., Zhao, S., Chen, B., Song, Y., Shao, Z., & Ni, M. (2022) · Nature Energy · doi:10.1038/s41560-022-01098-3 · Cited by 265
Predicting the thermodynamic stability of perovskite oxides using machine learning models
Li, W., Jacobs, R. M., & Morgan, D. D. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.04.033 · Cited by 264
Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors
Sahu, H., Rao, W., Troisi, A., & Ma, H. (2018) · Advanced Energy Materials · doi:10.1002/aenm.201801032 · Cited by 263
Jain, A., Hautier, G., Ong, S. P., & Persson, K. (2016) · Journal of Materials Research · doi:10.1557/jmr.2016.80 · Cited by 244
Accelerating materials discovery using machine learning
Juan, Y., Dai, Y., Yang, Y., & Zhang, J. (2021) · Journal of Materials Science & Technology · doi:10.1016/j.jmst.2020.12.010 · Cited by 240
Li, Z., Xu, Q., Sun, Q., Hou, Z., & Yin, W. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201807280 · Cited by 233
A universal strategy for the creation of machine learning-based atomistic force fields
Huan, T. D., Batra, R., Chapman, J., Krishnan, S., Chen, L., & Ramprasad, R. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0042-y · Cited by 229
Machine Learning in Nanoscience: Big Data at Small Scales
Brown, K. A., Brittman, S., Maccaferri, N., Jariwala, D., & Celano, U. (2020) · Nano Letters · doi:10.1021/acs.nanolett.9b04090 · Cited by 229
Freely Available Conformer Generation Methods: How Good Are They?
Ebejer, J.-P., Morris, G. M., & Deane, C. M. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci2004658 · Cited by 228
Li, J., Pradhan, B., Gaur, S., & Thomas, J. (2019) · Advanced Energy Materials · doi:10.1002/aenm.201901891 · Cited by 220
Machine learning accurate exchange and correlation functionals of the electronic density
Dick, S., & Fernández-Serra, M. (2020) · Nature Communications · doi:10.1038/s41467-020-17265-7 · Cited by 217
Finding New Perovskite Halides via Machine Learning
Pilania, G., Balachandran, P. V., Kim, C., & Lookman, T. (2016) · Frontiers in Materials · doi:10.3389/fmats.2016.00019 · Cited by 209
Data preprocessing in predictive data mining
Alexandropoulos, S.-A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019) · The Knowledge Engineering Review · doi:10.1017/s026988891800036x · Cited by 209
Dynamic recurrent neural networks: Theory and applications
Giles, C. L., Kuhn, G. M., & Williams, R. J. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1994.8753425 · Cited by 209
Innovative Materials Science via Machine Learning
Gao, C., Min, X., Fang, M., Tao, T., Zheng, X., Liu, Y., Wu, X., & Huang, Z. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202108044 · Cited by 198
Self-citationAn Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
Vo, A. H., Vleet, T. R. V., Gupta, R. R., Liguori, M. J., & Rao, M. S. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00227 · Cited by 197
A critical examination of compound stability predictions from machine-learned formation energies
Bartel, C., Trewartha, A., Wang, Q., Dunn, A., Jain, A., & Ceder, G. (2020) · eScholarship (California Digital Library) · Cited by 194
How machine learning can help select capping layers to suppress perovskite degradation
Hartono, N. T. P., Thapa, J., Tiihonen, A., Oviedo, F., Batali, C., Yoo, J. J., Liu, Z., Li, R., Marrón, D. F., Bawendi, M. G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17945-4 · Cited by 189
Zhao, Y., Zhang, J., Xu, Z., Sun, S., Langner, S., Hartono, N. T. P., Heumueller, T., Hou, Y., Elia, J., Li, N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22472-x · Cited by 184
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
Bommert, A., Welchowski, T., Schmid, M., & Rahnenführer, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab354 · Cited by 182
Mamy, L., Patureau, D., Barriuso, E., Bedos, C., Bessac, F., Louchart, X., Martin-laurent, F., Miege, C., & Benoit, P. (2015) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2014.955627 · Cited by 177
Review Study of Interpretation Methods for Future Interpretable Machine Learning
Mi, J., Li, A., & Zhou, L. (2020) · IEEE Access · doi:10.1109/access.2020.3032756 · Cited by 168
Saidi, W. A., Shadid, W., & Castelli, I. E. (2020) · npj Computational Materials · doi:10.1038/s41524-020-0307-8 · Cited by 164
Emerging perovskite materials for high density data storage and artificial synapses
Wang, Y., Lv, Z., Zhou, L., Chen, X., Chen, J., Zhou, Y., Roy, V. A. L., & Han, S. (2018) · Journal of Materials Chemistry C · doi:10.1039/c7tc05326f · Cited by 162
Machine learning aided design of perovskite oxide materials for photocatalytic water splitting
Tao, Q., Lu, T., Ye, S., Li, L., Lu, W., & Li, M. (2021) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2021.01.035 · Cited by 158
Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools
Odabaşı, Ç., & Yıldırım, R. (2018) · Nano Energy · doi:10.1016/j.nanoen.2018.11.069 · Cited by 150
Talapatra, A., Uberuaga, B. P., Stanek, C. R., & Pilania, G. (2021) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c03402 · Cited by 149
Neural Network for Nanoscience Scanning Electron Microscope Image Recognition
Modarres, M. H., Aversa, R., Cozzini, S., Ciancio, R., Leto, A., & Brandino, G. P. (2017) · Scientific Reports · doi:10.1038/s41598-017-13565-z · Cited by 145
Designing Molecules by Optimizing Potentials
Wang, M., Hu, X., Beratan, D. N., & Yang, W. (2006) · Journal of the American Chemical Society · doi:10.1021/ja0572046 · Cited by 145
Explaining the Explainer: A First Theoretical Analysis of LIME
Garreau, D., & von Luxburg, U. (2020) · arXiv · doi:10.48550/arxiv.2001.03447 · Cited by 143
Recent progress in perovskite solar cells: from device to commercialization
Luo, X., Lin, X., Gao, F., Zhao, Y., Li, X., Zhan, L., Qiu, Z., Wang, J., Chen, C., Meng, L., et al. (2022) · Science China Chemistry · doi:10.1007/s11426-022-1426-x · Cited by 142
Self-citationTensorFlow: learning functions at scale
Abadi, M. (2016) · ACM SIGPLAN Notices · doi:10.1145/3022670.2976746 · Cited by 139
Explainable Artificial Intelligence for Tabular Data: A Survey
Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135
Higgins, K., Valleti, S. M. P., Ziatdinov, M. A., Kalinin, S. V., & Ahmadi, M. (2020) · ACS Energy Letters · doi:10.1021/acsenergylett.0c01749 · Cited by 132
Avoiding common pitfalls in machine learning omic data science
Teschendorff, A. E. (2019) · Nature Materials · doi:10.1038/s41563-018-0241-z · Cited by 132
Recent progress of integrated circuits and optoelectronic chips
Hao, Y., Xiang, S., Han, G., Zhang, J., Ma, X., Zhu, Z., Guo, X., Zhang, Y., Han, Y., Song, Z., et al. (2021) · Science China Information Sciences · doi:10.1007/s11432-021-3235-7 · Cited by 132
Self-citationYang, Y., Zhang, R., Li, Z., Mei, L., Wan, S., Ding, H., Chen, Z., Xing, J., Feng, H., Han, J., et al. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b01721 · Cited by 132
Machine learning analysis on stability of perovskite solar cells
Odabaşı, Ç., & Yıldırım, R. (2019) · Solar Energy Materials and Solar Cells · doi:10.1016/j.solmat.2019.110284 · Cited by 127
Application of machine learning for advanced material prediction and design
Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123
Screening stable and metastable ABO3 perovskites using machine learning and the materials project
Liu, H., Cheng, J., Dong, H., Feng, J., Pang, B., Tian, Z., Ma, S., Xia, F., Zhang, C., & Dong, L. (2020) · Computational Materials Science · doi:10.1016/j.commatsci.2020.109614 · Cited by 120
Materials, methods and strategies for encapsulation of perovskite solar cells: From past to present
Raman, R. K., Thangavelu, S. A. G., Venkataraj, S., et al. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111608 · Cited by 117
Lyu, R., Moore, C. E., Liu, T., Yu, Y., & Wu, Y. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c05441 · Cited by 114
Khan, P. M., & Roy, K. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1542428 · Cited by 113
Yu, Y., Tan, X., Ning, S., & Wu, Y. (2019) · ACS Energy Letters · doi:10.1021/acsenergylett.8b02451 · Cited by 110
Self-citationChowdhury, A., Yang, W., Walker, E. A., Mamun, O., Heyden, A., & Terejanu, G. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.8b09284 · Cited by 110
TAKAHASHI, K. G., Takahashi, L., Miyazato, I., & Tanaka, Y. (2018) · ACS Photonics · doi:10.1021/acsphotonics.7b01479 · Cited by 109
Li, Y., Lu, Y., Huo, X., Wei, D., Meng, J., Dong, J., Qiao, B., Zhao, S., Xu, Z., & Song, D. (2021) · RSC Advances · doi:10.1039/d1ra03117a · Cited by 108
Jain, D., Chaube, S., Khullar, P., Goverapet Srinivasan, S., & Rai, B. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp03240a · Cited by 105
Wavelet Scattering Regression of Quantum Chemical Energies
Hirn, M., Mallat, S., & Poilvert, N. (2017) · Multiscale Modeling and Simulation · doi:10.1137/16m1075454 · Cited by 103
Zhao, Z.-W., del Cueto, M., Geng, Y., & Troisi, A. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c02325 · Cited by 102
Recent Advances in Halide-Based Perovskite Crystals and Their Optoelectronic Applications
Babu, R., Giribabu, L., & Singh, S. P. (2018) · Crystal Growth & Design · doi:10.1021/acs.cgd.7b01767 · Cited by 102
Rezaee, E., Zhang, W., & Silva, S. R. P. (2021) · Small · doi:10.1002/smll.202008145 · Cited by 102
Gao, Z., Zhang, H., Mao, G., Ren, J., Chen, Z., Wu, C., Gates, I. D., Yang, W., Ding, X., & Yao, J. (2021) · Applied Surface Science · doi:10.1016/j.apsusc.2021.150916 · Cited by 101
Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime
Sendek, A. D., Ransom, B., Cubuk, E. D., Pellouchoud, L. A., Nanda, J., & Reed, E. J. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202200553 · Cited by 100
Learning to Approximate Density Functionals
Kalita, B., Li, L., McCarty, R. J., & Burke, K. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00742 · Cited by 97
Doping in inorganic perovskite for photovoltaic application
Ma, X., Yang, L., Lei, K., et al. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105354 · Cited by 96
Polymers and Polymer Nanocomposites for Cancer Therapy
Feldman, D. (2019) · Applied Sciences · doi:10.3390/app9183899 · Cited by 95
Guo, Z., & Lin, B. (2021) · Solar Energy · doi:10.1016/j.solener.2021.09.030 · Cited by 95
Priya, P., & Aluru, N. R. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00551-3 · Cited by 94
Cross-Validation With Confidence
Lei, J. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1672556 · Cited by 93
Statistical analysis of proteomics data: A review on feature selection
Lualdi, M., & Fasano, M. (2019) · Journal of Proteomics · doi:10.1016/j.jprot.2018.12.004 · Cited by 93
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
Hernández, A., Balasubramanian, A., Yuan, F., Mason, S., & Mueller, T. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0249-1 · Cited by 91
Substance of Civilization: Materials and Human History from the Stone Age to the Age of Silicon
Cohen, E. A., & Sass, S. L. (1998) · Foreign Affairs · doi:10.2307/20048999 · Cited by 89
A survey on online feature selection with streaming features
Hu, X., Zhou, P., Li, P., Wang, J., & Wu, X. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-016-5489-3 · Cited by 88
Machine‐Learning Modeling for Ultra‐Stable High‐Efficiency Perovskite Solar Cells
Hu, Y., Hu, X., Zhang, L., Zheng, T., You, J., Jia, B., Ma, Y., Du, X., Zhang, L., Wang, J., et al. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202201463 · Cited by 88
How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells
Liu, Y., Yan, W., Han, S., Zhu, H., Tu, Y., Guan, L., & Tan, X. (2022) · Solar RRL · doi:10.1002/solr.202101100 · Cited by 87
Self-citationMaterials and Methods for High‐Efficiency Perovskite Solar Modules
Lee, D.-K., & Park, N.-G. (2022) · Solar RRL · doi:10.1002/solr.202100455 · Cited by 86
Cai, X., Zhang, Y., Shi, Z., Chen, Y., Xia, Y., Yu, A., Xu, Y., Xie, F., Shao, H., Zhu, H., et al. (2022) · Advanced Science · doi:10.1002/advs.202103648 · Cited by 84
Zhang, L., Zhou, X., Liu, C., Wang, X., & Xu, B. (2020) · Small Methods · doi:10.1002/smtd.202000254 · Cited by 83
Lu, Y., Wei, D., Liu, W., Meng, J., Huo, X., Zhang, Y., Liang, Z., Qiao, B., Zhao, S., Song, D., et al. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2022.10.024 · Cited by 83
Application of SMILES Notation Based Optimal Descriptors in Drug Discovery and Design
Veselinovic, A., Veselinovic, J., Zivkovic, J., & Nikolic, G. (2015) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026615666150506151533 · Cited by 82
Machine learning enabled development of unexplored perovskite solar cells with high efficiency
Yan, W., Liu, Y., Zang, Y., Cheng, J., Wang, Y., Chu, L., Tan, X., Liu, L., Zhou, P., Li, W., et al. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107394 · Cited by 82
Self-citationApplications of machine learning in perovskite materials
Wang, Z., Yang, M., Xie, X., Yu, C., Jiang, Q., Huang, M., Algadi, H., Guo, Z., & Zhang, H. (2022) · Advanced Composites and Hybrid Materials · doi:10.1007/s42114-022-00560-w · Cited by 81
Annealing Engineering in the Growth of Perovskite Grains
Wang, L., Liu, G., Xi, X., Yang, G., Hu, L., Zhu, B., He, Y., Liu, Y., Qian, H., Zhang, S., et al. (2022) · Crystals · doi:10.3390/cryst12070894 · Cited by 78
Self-citationAlgorithmic Splitting: A Method for Dataset Preparation
Kahloot, K. M., & Ekler, P. (2021) · IEEE Access · doi:10.1109/access.2021.3110745 · Cited by 77
Islam, M. S., Islam, M. T., Sarker, S., Jame, H. A., Nishat, S. S., Jani, M. R., Rauf, A., Ahsan, S., Shorowordi, K. M., Efstathiadis, H., et al. (2022) · ACS Omega · doi:10.1021/acsomega.2c01076 · Cited by 76
Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning
Li, X., Dan, Y., Dong, R., Cao, Z., Niu, C., Song, Y., Li, S., & Hu, J. (2019) · Applied Sciences · doi:10.3390/app9245510 · Cited by 74
Data-driven design of novel halide perovskite alloys
Mannodi-Kanakkithodi, A., & Chan, M. K. Y. (2022) · Energy & Environmental Science · doi:10.1039/d1ee02971a · Cited by 73
Topological feature engineering for machine learning based halide perovskite materials design
Anand, D. V., Xu, Q., Wee, J., Xia, K., & Sum, T. C. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00883-8 · Cited by 72
Zhang, S., Lu, T., Xu, P., Tao, Q., Li, M., & Lu, W. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c01939 · Cited by 71
Feature Fusion Using Locally Linear Embedding for Classification
Sun, B., Zhang, X.-M., Li, J., et al. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2036363 · Cited by 71
Development and Challenges of Metal Halide Perovskite Solar Modules
Cheng, Y., Peng, Y., Jen, A. K. ‐., & Yip, H. (2021) · Solar RRL · doi:10.1002/solr.202100545 · Cited by 69
Zafar, M. R., & Khan, N. M. (2019) · arXiv · doi:10.48550/arxiv.1906.10263 · Cited by 69
Cai, X., Liu, F., Yu, A., Qin, J., Hatamvand, M., Ahmed, I., Luo, J., Zhang, Y., Zhang, H., & Zhan, Y. (2022) · Light Science & Applications · doi:10.1038/s41377-022-00924-3 · Cited by 68
Contact Engineering: Electrode Materials for Highly Efficient and Stable Perovskite Solar Cells
Xiao, J., Shi, C., Zhou, C., et al. (2017) · Solar RRL · doi:10.1002/solr.201700082 · Cited by 67
Research works printed but not linked: 10
- Mauri A., 2006, MATCH Commun. Math. Comput. Chem., 56, 237
- N. K.Chauhan K.Singh in2018 International Conference on Computing Power and Communication Technologies (GUCON) IEEE Piscataway NJ2018 340.
- Freund Y., 1996, Proc. 13th Int. Conf. Machine Learning, 13, 148
- M. R.Zafar N. M.Khan arXiv preprintarXiv:1906.10263 2019.
- D. A.Augusto H. J. C.Barbosa Proc. Sixth Brazilian Symposium on Neural Networks2000 1 173.
- G.Peake J.Wang Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining2018 19.
- Lundberg S., 2017, Adv. Neural Inform. Processing Syst., 30, 4765
- Klaise J., 2021, J. Mach. Learn. Res., 22, 8194
- B.Zhou A.Khosla A.Lapedriza A.Oliva A.Torralba Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR) IEEE Piscataway NJ2016 2921.
- Bak T., 2022, Int. J. of Precis. Eng. and Manuf.‐Green Tech
Cited by
Sorted by citations
Machine Learning Aided Design and Optimization of Thermal Metamaterials
Zhu, C., Bamidele, E. A., Shen, X., Zhu, G., & Li, B. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00708 · Cited by 208
Wang, C., Cheng, X., Luo, K. H., Nandakumar, K., Wang, Z., Ni, M., Bi, X. T., Zhang, J., & Wang, C. (2025) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2025.101010 · Cited by 91
Decoding recombination dynamics in perovskite solar cells: an in-depth critical review
Adhikari, R. D., Patel, M. J., Baishya, H., Yadav, D. K., Kalita, M., Alam, M., & Iyer, P. K. (2025) · Chemical Society Reviews · doi:10.1039/d4cs01231c · Cited by 85
Encapsulating perovskite solar cells for long-term stability and prevention of lead toxicity
Dipta, S. S., Rahim, M. A., & Uddin, A. (2024) · Applied Physics Reviews · doi:10.1063/5.0197154 · Cited by 71
Shang, Y., Xiong, Z., An, K., Hauch, J., Brabec, C. J., & Li, N. (2024) · Materials Genome Engineering Advances · doi:10.1002/mgea.28 · Cited by 64
Metadata
- Authors
- Liu, Y., Tan, X., Liang, J., Han, H., Xiang, P., & Yan, W.
- Affiliations
- China Three Gorges University (CN)
- Wuhan National Laboratory for Optoelectronics (CN)
- Corresponding author
- Tan, Xinyu
- Xiang, Peng
- Yan, Wensheng
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2023-02-15
- Volume
- 33
- Issue
- 17
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W4320919502
- Cited by
- 174
- References
- 273
- Citations by year
- 2026: 45 · 2025: 62 · 2024: 49 · 2023: 18
- FWCI
- 12.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Advanced Functional Materials (publishedVersion)
- Funders
- National Key Research and Development Program of China (2022YFB4200904)
- National Natural Science Foundation of China (U1765105, 52007104)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Perovskite Materials and Applications
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- perovskite solar cells, machine learning, materials screening, electrodes, physicochemical property prediction, data-driven materials design, device design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
