Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

Cites (273)

Items 201–264 of 264

1. Machine learning in materials design: Algorithm and application*
   Song, Zhilong, Chen, Xiwen, Meng, Fanbin, Cheng, Guanjian, Wang, Chen, Sun, Zhongti, Yin, Wan-Jian · 2020 · Chinese Physics B
2. Machine Learning Accelerated Recovery of the Cubic Structure in Mixed-Cation Perovskite Thin Films
   Adnan Ali, Heesoo Park, Raghvendra Mall, Brahim Aïssa, Stefano Sanvito, Halima Bensmail, Abdelhak Belaidi, Fedwa El‐Mellouhi · 2020 · Chemistry of Materials
3. Identification of the dominant recombination process for perovskite solar cells based on machine learning
   Le Corre, Vincent M., Sherkar, Tejas S., Koopmans, Marten, Koster, L. Jan Anton · 2021 · Cell Reports Physical Science
4. Rapid discovery of narrow bandgap oxide double perovskites using machine learning
   Xue Yang, Long Li, Qiuling Tao, Wencong Lu, Minjie Li · 2021 · Computational Materials Science
5. Discovery of direct band gap perovskites for light harvesting by using machine learning
   Rath, Smarak, Sudha Priyanga, G., Nagappan, N., Thomas, Tiju · 2022 · Computational Materials Science
6. Back-interface regulation for carbon-based perovskite solar cells
   Huiyin Zhang, Keke Song, Lianqing Zhu · 2020 · Carbon
7. Machine learning-guided search for high-efficiency perovskite solar cells with doped electron transport layers
   She, Chenglong, Huang, Qicheng, Chen, Cong, Jiang, Yue, Fan, Zhen, Gao, Jinwei · 2021 · Journal of Materials Chemistry A
8. Recent Progress in High‐efficiency Planar‐structure Perovskite Solar Cells
   Yang Zhao, Qiufeng Ye, Zema Chu · 2019 · Energy & environment materials
9. Study on bandgap predications of ABX3-type perovskites by machine learning
   Liu, Yiming, Yan, Wensheng, Zhu, Heng, Tu, Yiteng, Guan, Li, Tan, Xinyu · 2022 · Organic Electronics
10. Magnetism in Double Perovskites
    Tanusri Saha‐Dasgupta · 2012 · Journal of Superconductivity and Novel Magnetism
11. Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives
    Myung, Chang Woo, Hajibabaei, Amir, Cha, Ji‐Hyun, Ha, Miran, Kim, Junu, Kim, Kwang S. · 2022 · Advanced Energy Materials
12. Assessment of Reproducibility, Hysteresis, and Stability Relations in Perovskite Solar Cells Using Machine Learning
    Çağla Odabaşı, Ramazan Yıldırım · 2020 · Energy Technology
13. Perovskite or Not Perovskite? A Deep‐Learning Approach to Automatically Identify New Hybrid Perovskites from X‐ray Diffraction Patterns
    Massuyeau, Florian, Broux, Thibault, Coulet, Florent, Demessence, Aude, Mesbah, Adel, Gautier, Romain · 2022 · Advanced Materials
14. Screening interface passivation materials intelligently through machine learning for highly efficient perovskite solar cells
    Liu, Wu, Lu, Yao, Wei, Dong, Huo, Xiaomin, Huang, Xiaofeng, Li, Yaoyao, Meng, Juan, Zhao, Suling, Qiao, Bo, Liang, Zhiqin et al. · 2022 · Journal of Materials Chemistry A
15. Feature Selection Boosted by Unselected Features
    Zheng, Wei, Chen, Shuo, Fu, Zhenyong, Zhu, Fa, Yan, Hui, Yang, Jian · 2022 · IEEE Transactions on Neural Networks and Learning Systems
16. Learn-and-Match Molecular Cations for Perovskites
    Park, Heesoo, Mall, Raghvendra, Alharbi, Fahhad H., Sanvito, Stefano, Tabet, Nouar, Bensmail, Halima, El-Mellouhi, Fedwa · 2019 · The Journal of Physical Chemistry A
17. Explainable AI in Healthcare and Medicine
    Maria H\"ugle, Gabriel Kalweit, Thomas Huegle · 2020 · Studies in computational intelligence
18. Designing Two-Dimensional Halide Perovskites Based on High-Throughput Calculations and Machine Learning
    Hu, Wenguang, Zhang, Lei, Pan, Zheng · 2022 · ACS Applied Materials & Interfaces
19. Learning an Optimal Naive Bayes Classifier
    Miriam Martı́nez-Arroyo, Luis Enrique Sucar · 2006 · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition
20. Bayesian optimization based on a unified figure of merit for accelerated materials screening: A case study of halide perovskites
    Chen, Xiwen, Wang, Chen, Li, Zhenzhu, Hou, Zhufeng, Yin, Wan-Jian · 2020 · Science China Materials
21. Accelerated design of promising mixed lead-free double halide organic–inorganic perovskites for photovoltaics using machine learning
    Wu, Yilei, Lu, Shuaihua, Ju, Ming-Gang, Zhou, Qionghua, Wang, Jinlan · 2021 · Nanoscale
22. A machine learning model for screening thermodynamic stable lead-free halide double perovskites
    Liang, Gui-Qin, Zhang, Jian · 2022 · Computational Materials Science
23. An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites
    Wang, Zhilong, Han, Yanqiang, Lin, Xirong, Cai, Junfei, Wu, Sicheng, Li, Jinjin · 2022 · ACS Applied Materials & Interfaces
24. Deep Learning for Additive Screening in Perovskite Light‐Emitting Diodes
    Liang Zhang, Na Li, Dawei Liu, Guanhong Tao, Weidong Xu, Mengmeng Li, Ying Chu, Chensi Cao, Feiyue Lu, Chenjie Hao et al. · 2022 · Angewandte Chemie International Edition
25. Self-spreading produces highly efficient perovskite solar cells
    Ling Liu, Chuantian Zuo, Liming Ding · 2021 · Nano Energy
26. Carbon nanomaterials–polymer composites for perovskite solar cells: preparation, properties and applications
    Qianqian Dou, Tanner Whatley, Tajamul Hussain Syed · 2022 · Journal of Materials Chemistry A
27. Bayesian Optimization of photonic curing process for flexible perovskite photovoltaic devices
    Xu, Weijie, Liu, Zhe, Piper, Robert T., Hsu, Julia W.P. · 2023 · Solar Energy Materials and Solar Cells
28. Importance of structural deformation features in the prediction of hybrid perovskite bandgaps
    Park, Heesoo, Mall, Raghvendra, Ali, Adnan, Sanvito, Stefano, Bensmail, Halima, El-Mellouhi, Fedwa · 2020 · Computational Materials Science
29. Multiple Classifier Systems
    MARCIALIS, GIAN LUCA, Fabio Roli · 2004 · Lecture notes in computer science
30. Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance
    del Cueto, Marcos, Rawski-Furman, Charles, Aragó, Juan, Ortí, Enrique, Troisi, Alessandro · 2022 · The Journal of Physical Chemistry C
31. Comparative analysis of machine learning approaches on the prediction of the electronic properties of perovskites: A case study of ABX3 and A2BB’X6
    Ericsson Tetteh Chenebuah, Michel Nganbe, Alain Tchagang · 2021 · Materials Today Communications
32. Studies on the regularity of perovskite formation via machine learning
    Li, Long, Tao, Qiuling, Xu, Pengcheng, Yang, Xue, Lu, Wencong, Li, MinJie · 2021 · Computational Materials Science
33. Screening Perovskites from ABO₃ Combinations Generated by Constraint Satisfaction Techniques Using Machine Learning
    Zhao, Jie, Wang, Xiaoyan · 2022 · ACS Omega
34. Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
    Caiafa, Cesar Federico, Solé-Casals, Jordi, Marti-Puig, Pere, Zhe, Sun, Tanaka, Toshihisa · 2020 · Applied Sciences
35. Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning
    Mannodi-Kanakkithodi, Arun, Chan, Maria K. Y. · 2022 · Journal of Materials Science
36. Discriminant Independent Component Analysis
    Chandra Dhir, Soo Young Lee · 2011 · IEEE Transactions on Neural Networks
37. Improved physics-based structural descriptors of perovskite materials enable higher accuracy of machine learning
    Li, Changjiao, Hao, Hua, Xu, Ben, Shen, Zhonghui, Zhou, Enhao, Jiang, Dongbing, Liu, Hanxing · 2021 · Computational Materials Science
38. Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts
    Mai, Haoxin, Le, Tu C., Hisatomi, Takashi, Chen, Dehong, Domen, Kazunari, Winkler, David A., Caruso, Rachel A. · 2021 · iScience
39. Halide Double Perovskite Ferroelectrics
    Weichuan Zhang, Maochun Hong, Junhua Luo · 2020 · Angewandte Chemie
40. Research progress of atomic layer deposition technology to improve the long-term stability of perovskite solar cells
    Yifan Yang, Yujing Zhang, Luyun Bai · 2021 · Journal of Materials Chemistry C
41. Machine Learning Accelerates the Discovery of Light-Absorbing Materials for Double Perovskite Solar Cells
    Zhenqing Yang, Yanbing Liu, Yun‐Qian Zhang, Liqun Wang, Chundan Lin, Yuzhuo Lv, Yuhang Ma, Changjin Shao · 2021 · The Journal of Physical Chemistry C
42. Efficiency and Stability Analysis of 2D/3D Perovskite Solar Cells Using Machine Learning
    Yılmaz, Beyza, Odabaşı, Çağla, Yıldırım, Ramazan · 2022 · Energy Technology
43. High-Working-Pressure Sputtering of ZnO for Stable and Efficient Perovskite Solar Cells
    Abhishek Thote, Il Jeon, Hao‐Sheng Lin et al. · 2019 · ACS Applied Electronic Materials
44. Machine learning of octahedral tilting in oxide perovskites by symbolic classification with compressed sensing
    Stephen Xie, Parker L. Kotlarz, Richard G. Hennig, Juan C. Nino · 2020 · Computational Materials Science
45. Quick screening stable double perovskite oxides for photovoltaic applications by machine learning
    Liu, Haiying, Feng, Jianguang, Dong, Lifeng · 2022 · Ceramics International
46. Active meta-learning for predicting and selecting perovskite crystallization experiments
    Shekar, Venkateswaran, Nicholas, Gareth, Najeeb, Mansoor Ani, Zeile, Margaret, Yu, Vincent, Wang, Xiaorong, Slack, Dylan, Li, Zhi, Nega, Philip W., Chan, Emory M. et al. · 2022 · The Journal of Chemical Physics
47. PSO-SVR predicting for the Ehull of ABO3-type compounds to screen the thermodynamic stable perovskite candidates based on multi-scale descriptors
    Lanping Chen, Xuechen Wang, Wenjie Xia, Changhai Liu · 2022 · Computational Materials Science
48. Multiple Classifier Systems
    Josef Kittler · 2002 · Series in machine perception and artificial intelligence
49. A group lasso based sparse KNN classifier
    Zheng, Shuai, Ding, Chris · 2020 · Pattern Recognition Letters
50. A Machine Learning‐Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite‐Based Materials
    Jaafreh, Russlan, Sharan, Abhishek, Sajjad, Muhammad, Singh, Nirpendra, Hamad, Kotiba · 2023 · Advanced Functional Materials
51. Evaluation of performance of machine learning methods in mining structure–property data of halide perovskite materials
    Zhao, Ruoting, Xing, Bangyu, Mu, Huimin, Fu, Yuhao, Zhang, Lijun · 2022 · Chinese Physics B
52. Large-area perovskite films for PV applications: A perspective from nucleation and crystallization
    Yuanhang Yang, Zexu Xue, Long Chen · 2020 · Journal of Energy Chemistry
53. Machine learning prediction of 2D perovskite photovoltaics and interaction with energetic ion implantation
    Feng, Hong-Jian, Ma, Ping · 2021 · Applied Physics Letters
54. Interface compatibility: how to outperform classical spiro-OMeTAD in perovskite solar cells with carbazole derivatives
    Zeinab Kadi, Ru Wang, Nicolas Berton · 2022 · Journal of Materials Chemistry C
55. Simple Structural Descriptor Obtained from Symbolic Classification for Predicting the Oxygen Vacancy Defect Formation of Perovskites
    Liu, Siyu, Wang, Jing, Duan, Zhongtao, Wang, Kongxiang, Zhang, Wanlu, Guo, Ruiqian, Xie, Fengxian · 2022 · ACS Applied Materials & Interfaces
56. Data-driven analysis of the electronic-structure factors controlling the work functions of perovskite oxides
    Xiong, Yihuang, Chen, Weinan, Guo, Wenbo, Wei, Hua, Dabo, Ismaila · 2021 · Physical Chemistry Chemical Physics
57. Multivariate Analysis of Mixed Ternary and Quaternary A-Site Organic Cations in Tin Iodide Perovskite Solar Cells
    Nakanishi, Eita, Nishikubo, Ryosuke, Ishiwari, Fumitaka, Nakamura, Tomoya, Wakamiya, Atsushi, Saeki, Akinori · 2022 · ACS Materials Letters
58. Data‐Driven Investigation of the Synthesizability and Bandgap of Double Perovskite Halides
    Kim, Joonchul, Min, Kyoungmin · 2022 · Advanced Theory and Simulations
59. Principled Exploration of Bipyridine and Terpyridine Additives to Promote Methylammonium Lead Iodide Perovskite Crystallization
    Hartono, Noor Titan Putri, Ani Najeeb, Mansoor, Li, Zhi, Nega, Philip W., Fleming, Clare A., Sun, Xiaohe, Chan, Emory M., Abate, Antonio, Norquist, Alexander J., Schrier, Joshua et al. · 2022 · Crystal Growth & Design
60. ErtlFunctionalGroupsFinder: automated rule-based functional group detection with the Chemistry Development Kit (CDK)
    Sebastian Fritsch, Stefan Neumann, Jonas Schaub, Christoph Steinbeck, Achim Zielesny · 2019 · Journal of Cheminformatics
61. Monitoring the role of site chemistry on the formation energy of perovskites <i>via</i> deep learning analysis of Hirshfeld surfaces
    Williams, Logan, Mukherjee, Arpan, Dasgupta, Aparajita, Rajan, Krishna · 2021 · Journal of Materials Chemistry C
62. Computation of photovoltaic and stability properties of hybrid organic–inorganic perovskites via convolutional neural networks
    Aristizabal-Ferreira, Victor Alexander, Guevara-Vela, José Manuel, Sauza-de la Vega, Arturo, Pendás, Ángel Martín, Fuentes-Pineda, Gibran, Rocha-Rinza, Tomás · 2022 · Theoretical Chemistry Accounts
63. Machine learning and first-principles insights on molecularly modified CH3NH3PbI3 film in water
    Lei Zhang, Mu He, Wenguang Hu · 2022 · Applied Surface Science
64. The materials data ecosystem: Materials data science and its role in data-driven materials discovery
    Yin, Hai-Qing, Jiang, Xue, Liu, Guo-Quan, Elder, Sharon, Xu, Bin, Zheng, Qing-Jun, Qu, Xuan-Hui · 2018 · Chinese Physics B