The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Recent advances and applications of machine learning in solid-state materials science
Open accessAbstract
One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Cited by
Oses, C., Toher, C., & Curtarolo, S. (2020) · Nature Reviews Materials · doi:10.1038/s41578-019-0170-8 · Cited by 2,081
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
“Testing and benchmarking methodologies such as under fi tting/over fi tting/cross-validation 15,16,37 are common knowledge, and standards for measuring model performance are well established in the community.”
MethodHart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703
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
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
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
A generative model for inorganic materials design
Zeni, C., Pinsler, R., Zügner, D., Fowler, A., Horton, M., Fu, X., Wang, Z., Shysheya, A., Crabbé, J., Ueda, S., et al. (2025) · Nature · doi:10.1038/s41586-025-08628-5 · Cited by 518
Pyzer-Knapp, E. O., Pitera, J. W., Staar, P. W. J., Takeda, S., Laino, T., Sanders, D. P., Sexton, J., Smith, J. R., & Curioni, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00765-z · Cited by 465
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446
Dunn, A., Wang, Q., Ganose, A., Dopp, D., & Jain, A. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00406-3 · Cited by 411
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
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
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
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
Evaluation framework to guide implementation of AI systems into healthcare settings
Reddy, S., Rogers, W., Makinen, V.-P., Coiera, E., Brown, P., Wenzel, M., Weicken, E., Ansari, S., Mathur, P., Casey, A., et al. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100444 · Cited by 230
“16 This validation process includes the classical concept of goodness- of-fit, but also”
MethodJablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., et al. (2023) · Digital Discovery · doi:10.1039/d3dd00113j · Cited by 218
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
Explainable artificial intelligence in Alzheimer’s disease classification: a systematic review
Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10192-x · Cited by 195
“In the last decade, several ML and DL algorithms have achieved breakthrough results in various AI-based decision-making, such as disease prognosis and prediction [92], drug discovery and development [93], solid-state material science [94] and machine fault analysis [95].”
Zeolite (In)Stability under Aqueous or Steaming Conditions
Heard, C. J., Grajciar, L., Uhlík, F., Shamzhy, M., Opanasenko, M., Čejka, J., & Nachtigall, P. (2020) · Advanced Materials · doi:10.1002/adma.202003264 · Cited by 157
Muhammad, W., Brahme, A. P., Ibragimova, O., Kang, J., & Inal, K. (2021) · International Journal of Plasticity · doi:10.1016/j.ijplas.2020.102867 · Cited by 149
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
Schlicker, N., Langer, M., Ötting, S. K., Baum, K., König, C. J., & Wallach, D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106837 · Cited by 106
Machine Learning Approaches for Auto Insurance Big Data
Hanafy, M., & Ming, R. (2021) · Risks · doi:10.3390/risks9020042 · Cited by 102
Machine learning-enabled optimization of extrusion-based 3D printing
Rahmani Dabbagh, S., Ozcan, O., & Tasoglu, S. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.002 · Cited by 94
Deep learning for automated size and shape analysis of nanoparticles in scanning electron microscopy
Bals, J., & Epple, M. (2023) · RSC Advances · doi:10.1039/d2ra07812k · Cited by 93
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
Scientific Exploration and Explainable Artificial Intelligence
Zednik, C., & Boelsen, H. (2022) · Minds and Machines · doi:10.1007/s11023-021-09583-6 · Cited by 80
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
Toward Interpretable Machine Learning Models for Materials Discovery
Mikulskis, P., Alexander, M. R., & Winkler, D. A. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900045 · Cited by 43
Importance of structural deformation features in the prediction of hybrid perovskite bandgaps
Park, H., Mall, R., Ali, A., Sanvito, S., Bensmail, H., & El-Mellouhi, F. (2020) · Computational Materials Science · doi:10.1016/j.commatsci.2020.109858 · Cited by 39
Mannodi-Kanakkithodi, A., & Chan, M. K. Y. (2022) · Journal of Materials Science · doi:10.1007/s10853-022-06998-z · Cited by 33
Interpretable Deep-Learning Unveils Structure–Property Relationships in Polybenzenoid Hydrocarbons
Weiss, T., Wahab, A., Bronstein, A. M., & Gershoni-Poranne, R. (2023) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.2c02381 · Cited by 24
Current challenges in adopting machine learning to critical care and emergency medicine
Kang, C.-Y., & Yoon, J. H. (2023) · Clinical and Experimental Emergency Medicine · doi:10.15441/ceem.23.041 · Cited by 20
Active learning of neural network potentials for rare events
(2024) · doi:10.1039/d3dd00216k · Cited by 20
Liu, S., Wang, J., Duan, Z., Wang, K., Zhang, W., Guo, R., & Xie, F. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c24003 · Cited by 17
Data‐Driven Investigation of the Synthesizability and Bandgap of Double Perovskite Halides
Kim, J., & Min, K. (2022) · Advanced Theory and Simulations · doi:10.1002/adts.202200068 · Cited by 14
AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning
Morán-González, L., Betten, J. E., Kneiding, H., & Balcells, D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01583 · Cited by 12
A Survey on Data-Driven Methods for State of Charge Estimation of Battery
Satyan, P. A., & Sutar, R. G. (2020) · 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc48915.2020.9155926 · Cited by 12
Velpandian, M., & Basu, S. (2025) · Indian Chemical Engineer · doi:10.1080/00194506.2024.2444394 · Cited by 9
Performance-Based Screening of Porous Materials for Carbon Capture
Farmahini, A. H., Krishnamurthy, S., Friedrich, D., Brandani, S., & Sarkisov, L. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01266 · Cited by 7
Anton, O., & Stirnemann, G. (2026) · ChemSystemsChem · doi:10.1002/syst.202500057 · Cited by 4
Enhancing high-fidelity neural network potentials through low-fidelity sampling
Jung, G. S. (2024) · APL Machine Learning · doi:10.1063/5.0222779 · Cited by 2
Metadata
- Authors
- Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L.
- Venue
- npj Computational Materials
- Published
- 2019-08-08
- Volume
- 5
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2968923792
- Cited by
- 2,607
- References
- 59
- FWCI
- 91.79
- Type
- journal article
- Language
- en
- Open access
- gold
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.
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
