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RESEARCH PAPER · Crystals · 2021

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Ruoyu Li, Qin Deng, Dong Tian, Daoye Zhu, Bin Lin

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Predicting Perovskite Performance with Multiple Machine-Learning AlgorithmsOriginalPredicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques31Optical Characterization and Prediction with Neural Network Modeling of Various Stoichiometries of Perovskite Materials Using a Hyperregression Method9Predicting the formation and stability of oxide perovskites by extracting underlying mechanisms using machine learning24Technology of Radial Fluid Enhanced Diffusion Based on Machine Learning0Applications of Machine Learning to the Study of Crystalline Materials0A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A 2 B + B 3+ X 615Machine learning in energy chemistry: introduction, challenges and perspectives31Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27Bandgap prediction of ABX3-type perovskites using Broad Learning System12Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76A machine learning approach for in silico prediction of the photovoltaic properties of perovskite solar cells based on dopant-free hole-transport materials8Unraveling the Correlation between the Interface Structures and Tunable Magnetic Properties of La1–xSrxCoO3−δ/La1–xSrxMnO3−δ Bilayers Using Deep Learning Models4Enhancing performance of lower-temperature solid oxide fuel cell cathodes through surface engineering69Comparative Analysis of Conventional Machine Learning and Graph Neural Network Models for Perovskite Property Prediction16Machine Learning-Assisted Prediction of Stress Corrosion Crack Growth Rate in Stainless Steel18Machine Learning Approaches in Advancing Perovskite Solar Cells Research16A comprehensive review of machine learning applications in perovskite solar cells: Materials discovery, device performance, process optimization and systems integration41Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives92Machine learning research advances in energy storage polymer-based dielectrics11Machine Learning‐Driven Perovskite Research from Experimental Exploration to Industrial Development6A hybrid machine learning approach for predicting the performance of perovskite solar cells under varying temperatures2Genetic Algorithm-Based Hybrid Deep Learning Framework for Stability Prediction of ABO3 Perovskites in Solar Cell Applications8Formation Energy Prediction of Doped Perovskite Structures Based on Transfer Learning with Small Datasets2Tunable physical properties of A-site double perovskite KLaYBiO 6 under hydrostatic pressure: Implications for renewable energy technologies2Advances in additive engineering for stable and efficient light-absorbing layers of CsPbI3 perovskite solar cells1Data-driven AI approaches for screening high-efficiency, stable, and lead-free perovskite photovoltaic materials: a review2Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges7Prediction of Apple Canopy Leaf Area Index Based on Near-Infrared Spectroscopy and Machine Learning0A hybrid CNN–LSTM architecture for bandgap prediction in mixed-halide perovskites outperforming machine and deep learning models0Formation Energy Prediction and Feature Analysis of Lead-Free Double Perovskites Based on Deep Learning0

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  1. Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques

    Materials Today Proceedings · 2021-10-14

    Vidyasagar Shetty, Shabari Shedthi B, J. Kumaraswamy

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  2. Optical Characterization and Prediction with Neural Network Modeling of Various Stoichiometries of Perovskite Materials Using a Hyperregression Method

    Nanomaterials · 2022-03-11

    Soo Min Kim, Syed Dildar Haider Naqvi, Min Gu Kang, Hee‐eun Song, SeJin Ahn

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  3. Predicting the formation and stability of oxide perovskites by extracting underlying mechanisms using machine learning

    Computational Materials Science · 2022-06-01

    George Stephen Thoppil, Alankar Alankar

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  4. Technology of Radial Fluid Enhanced Diffusion Based on Machine Learning

    Lecture notes on data engineering and communications technologies · 2022-07-02

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  5. Applications of Machine Learning to the Study of Crystalline Materials

    Crystals · 2022-07-30

    Hartmut Schlenz, Stefan Sandfeld

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  6. A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A 2 B + B 3+ X 6

    Journal of Materials Chemistry A · 2023-01-01

    Xiao Wei, Yunong Zhang, Xi Liu, Junjie Peng, Shengzhou Li, Renchao Che, Huiran Zhang

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  7. Machine learning in energy chemistry: introduction, challenges and perspectives

    Energy Advances · 2023-01-01

    Yuzhi Xu, Jiankai Ge, Cheng‐Wei Ju

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  8. Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data

    Solar RRL · 2023-01-19

    Felix Laufer, Sebastian Ziegler, Fabian Schackmar, Edwin A. Moreno Viteri, Markus Götz, Charlotte Debus, Fabian Isensee, Ulrich W. Paetzold

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  9. Bandgap prediction of ABX3-type perovskites using Broad Learning System

    Materials Today Communications · 2023-09-04

    Tian Tian, Tao Li, Gen Li, Fuchong Hao, Rong Tang, Zifan Yuan, Xueqin Liu

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  10. Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives

    Energy Technology · 2023-09-13

    Nitin Kumar Bansal, Snehangshu Mishra, Himanshu Dixit, Shivam Porwal, Paulomi Singh, Trilok Singh

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  11. A machine learning approach for in silico prediction of the photovoltaic properties of perovskite solar cells based on dopant-free hole-transport materials

    New Journal of Chemistry · 2024-01-01

    Islam M. Abdellah, Ahmed El‐Shafei

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  12. Unraveling the Correlation between the Interface Structures and Tunable Magnetic Properties of La1–xSrxCoO3−δ/La1–xSrxMnO3−δ Bilayers Using Deep Learning Models

    ACS Applied Materials & Interfaces · 2024-05-23

    Hong Sun, Vincenzo Lordi, Yayoi Takamura, Amit Samanta

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  13. Enhancing performance of lower-temperature solid oxide fuel cell cathodes through surface engineering

    Progress in Materials Science · 2024-08-12

    Halefom G. Desta, Gebrehiwot Gebreslassie, Jianjian Zhang, Bin Lin, Yun Zheng, Yun Hong Zheng

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  14. Comparative Analysis of Conventional Machine Learning and Graph Neural Network Models for Perovskite Property Prediction

    The Journal of Physical Chemistry C · 2024-09-24

    Jirui Jin, Somayeh Faraji, Bin Liu, Mingjie Liu

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  15. Machine Learning-Assisted Prediction of Stress Corrosion Crack Growth Rate in Stainless Steel

    Crystals · 2024-09-27

    Peng Wang, Huanchun Wu, Xiangbing Liu, Chaoliang Xu

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  16. Machine Learning Approaches in Advancing Perovskite Solar Cells Research

    Advanced Theory and Simulations · 2024-11-02

    Subham Subba, Pratika Rai, Suman Chatterjee

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  17. A comprehensive review of machine learning applications in perovskite solar cells: Materials discovery, device performance, process optimization and systems integration

    Materials Today Energy · 2024-11-15

    Ling Mao, Changying Xiang

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  18. Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

    Advanced Materials · 2024-12-20

    Panpan Zhou, Qianwen Zhou, Xuezhang Xiao, Xiulin Fan, Yongjin Zou, Lixian Sun, Jinghua Jiang, Dan Song, Lixin Chen

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  19. Machine learning research advances in energy storage polymer-based dielectrics

    Computational Materials Science · 2025-01-11

    Qixin Yuan, Dong Yue, Zhe Zhang, Yu Feng, Qingguo Chen

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  20. Machine Learning‐Driven Perovskite Research from Experimental Exploration to Industrial Development

    Solar RRL · 2025-08-21

    Zhuo Feng, Xu Zhu, Meng Hao, Antai Yang, Jixin Tang, Chengquan Zhong, Kailong Hu, Jiakai Liu, Jingzi Zhang, Xi Lin

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  21. A hybrid machine learning approach for predicting the performance of perovskite solar cells under varying temperatures

    Energy Conversion and Management X · 2025-08-22

    Ali Rahmani, Farzin Hosseinifard, Mohsen Salimi, Majid Amidpour

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  22. Genetic Algorithm-Based Hybrid Deep Learning Framework for Stability Prediction of ABO3 Perovskites in Solar Cell Applications

    Energies · 2025-09-23

    Samad Wali, Muhammad Irfan Khan, Miao Zhang, Abdul Shakoor

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  23. Formation Energy Prediction of Doped Perovskite Structures Based on Transfer Learning with Small Datasets

    Crystals · 2025-11-24

    Yu Yang, Mingxuan Deng, Tianhao Rui, Zhuangzhuang Ma, Linyuan Lu, Yunhao Wang, Tao Lan, Yulin Lan, Hengcheng Wan, Yiyan Li, Zhipeng Li, Haibin Zhang

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  24. Tunable physical properties of A-site double perovskite KLaYBiO 6 under hydrostatic pressure: Implications for renewable energy technologies

    International Journal of Modern Physics B · 2025-12-09

    Hudabia Murtaza, Ahmed B. M. Ibrahim, Junaid Munir, Abhinav Kumar, Mohamed Ahmed Habib, Ankit D. Oza, Quratul Ain

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  25. Advances in additive engineering for stable and efficient light-absorbing layers of CsPbI3 perovskite solar cells

    Materials Today Chemistry · 2025-12-15

    Jinjie Zhou, Yaofang Zhang, Yuchen Lei, Yuxuan Wang, Yangfan Zhao, Fei Ding, Yu Yan, Nanping Deng, Jian Ni, Weiming Kang

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  26. Data-driven AI approaches for screening high-efficiency, stable, and lead-free perovskite photovoltaic materials: a review

    Frontiers in Materials · 2026-01-06

    Beibei Wang, Juan Wang, Liping Li, Dengwu Wang

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  27. Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges

    Advanced Science · 2026-03-24

    Yangfan Zhang, Yiming Xia, Ali Shakiba, Hongrui Zhang, Xiaojing Hao, Priyank V. Kumar, Mahesh P. Suryawanshi

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  28. Prediction of Apple Canopy Leaf Area Index Based on Near-Infrared Spectroscopy and Machine Learning

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  29. A hybrid CNN–LSTM architecture for bandgap prediction in mixed-halide perovskites outperforming machine and deep learning models

    Computational Condensed Matter · 2026-05-21

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  30. Formation Energy Prediction and Feature Analysis of Lead-Free Double Perovskites Based on Deep Learning

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