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RESEARCH PAPER · The Journal of Physical Chemistry Letters · 2022

Quantitative Predictions of Moisture-Driven Photoemission Dynamics in Metal Halide Perovskites via Machine Learning

John M. Howard, Qiong Wang, Meghna Srivastava, Tao Gong, Erica Lee, Antonio Abate, Marina S. Leite

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Quantitative Predictions of Moisture-Driven Photoemission Dynamics in Metal Halide Perovskites via Machine LearningOriginalPrediction of Initial Reaction Characteristics of Materials from Molecular Conformational Changes Based on Artificial Intelligence Technology2Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27Machine Learning Enables Prediction of Halide Perovskites’ Optical Behavior with >90% Accuracy44The role of machine learning in perovskite solar cell research42Transforming characterization data into information in the case of perovskite solar cells8Emerging opportunities for hybrid perovskite solar cells using machine learning8Diffusion in a bicrystal: effect of water uptake through grain boundary on the degradation of halide perovskites1Machine Learning‐Accelerated Development of Perovskite Optoelectronics Toward Efficient Energy Harvesting and Conversion3Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76Curated materials data of hybrid perovskites: approaches and potential usage3Machine learning–assisted colloidal synthesis: A review39High-Throughput Multi-Scale Experiments to Probe the Stability of Metal Halide Perovskites11Perovskite single crystal SCLC measurement prediction using a machine learning model10Physiochemical machine learning models predict operational lifetimes of CH 3 NH 3 PbI 3 perovskite solar cells25Accelerating discovery of tunable optical materials (ATOM)3Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent5Luminescent perovskite quantum dots: Progress in fabrication, modelling and machine learning approaches for advanced photonic and quantum computing applications23Machine Learning-Assisted Prediction of Ambient-Processed Perovskite Solar Cells’ Performances13Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication36High-performance perovskite quantum dot synthesis investigated through exploratory data analysis3Machine Learning Prediction of Organic–Inorganic Halide Perovskite Solar Cell Performance from Optical Properties42Comparative analysis of machine learning techniques for temperature and humidity prediction in photovoltaic environments53Unlocking a self-driving research workflow for perovskite photovoltaics12A hybrid machine learning approach for predicting the performance of perovskite solar cells under varying temperatures2Machine Learning Unlocks New Directions in Halide Perovskite Research6AI-driven analysis of harmonic intensity in perovskite single crystals0Efficient Discovery of Lead-Free A 3 BX 6 Halide Perovskites via Machine Learning2Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories1High‐Accuracy Machine Learning Projections of Composition‐Dependent Thermal Stability in Halide Perovskites0Opposite and reversible optical switching in soft-lattice halide perovskites using light and heat0

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  1. Prediction of Initial Reaction Characteristics of Materials from Molecular Conformational Changes Based on Artificial Intelligence Technology

    The Journal of Physical Chemistry C · 2022-11-18

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

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  3. Machine Learning Enables Prediction of Halide Perovskites’ Optical Behavior with >90% Accuracy

    ACS Energy Letters · 2023-03-10

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  4. The role of machine learning in perovskite solar cell research

    Journal of Alloys and Compounds · 2023-06-01

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  5. Transforming characterization data into information in the case of perovskite solar cells

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  6. Emerging opportunities for hybrid perovskite solar cells using machine learning

    APL Energy · 2023-07-05

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  7. Diffusion in a bicrystal: effect of water uptake through grain boundary on the degradation of halide perovskites

    Physica Scripta · 2023-08-22

    Fuqian Yang

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  8. Machine Learning‐Accelerated Development of Perovskite Optoelectronics Toward Efficient Energy Harvesting and Conversion

    Advanced Energy and Sustainability Research · 2023-08-30

    Baian Chen, Rui Chen, Bolong Huang

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  9. 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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  10. Curated materials data of hybrid perovskites: approaches and potential usage

    Trends in Chemistry · 2023-09-15

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  11. Machine learning–assisted colloidal synthesis: A review

    Materials Today Chemistry · 2023-12-07

    Dayana Gulevich, Igor R. Nabiev, Pavel S. Samokhvalov

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  12. High-Throughput Multi-Scale Experiments to Probe the Stability of Metal Halide Perovskites

    ACS Energy Letters · 2023-12-19

    Yalan Zhang, Yuanyuan Zhou

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  13. Perovskite single crystal SCLC measurement prediction using a machine learning model

    Journal of Materials Chemistry C · 2024-01-01

    Sarvani Jowhar Khanam, Harinath Kuruva, Salman Abdul Moiz, Banavoth Murali

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  14. Physiochemical machine learning models predict operational lifetimes of CH 3 NH 3 PbI 3 perovskite solar cells

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  15. Accelerating discovery of tunable optical materials (ATOM)

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    Trish Veeder, Arash Dehhzangi, Shriram Ramanathan, Mikhail A. Kats, Nanfang Yu, Juejun Hu, Christopher Roberts, Mark J. Polking, Kevin Tibbetts, Arka Majumdar, Marina S. Leite, Houman Homayoun, Jeremy N. Munday, K. K. Son

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  16. Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent

    Micromachines · 2024-08-26

    Yitong Wu, Sifan Kong, Qingxin Yao, Muyun Li, Huayi Lai, Duoyu Sun, Qingyue Cai, Zelin Qiu, Honglong Ning, Yong Zhang

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  17. Luminescent perovskite quantum dots: Progress in fabrication, modelling and machine learning approaches for advanced photonic and quantum computing applications

    Journal of Luminescence · 2024-09-23

    K. Deepthi Jayan, Kesiya Babu

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  18. Machine Learning-Assisted Prediction of Ambient-Processed Perovskite Solar Cells’ Performances

    Energies · 2024-11-28

    Dowon Pyun, Seungtae Lee, Solhee Lee, Seok-Hyun Jeong, Jae‐Keun Hwang, Kyunghwan Kim, Young‐Min Kim, Jiyeon Nam, Sujin Cho, Ji-Seong Hwang, Wonkyu Lee, Sang‐Won Lee, Haeseok Lee, Dong‐Hwan Kim, Yoonmook Kang

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  19. Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication

    Energy & Environmental Science · 2025-01-01

    Felix Laufer, Markus Götz, Ulrich W. Paetzold

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  20. High-performance perovskite quantum dot synthesis investigated through exploratory data analysis

    Nanoscale · 2025-01-01

    Sabah Gaznaghi, Nick Dashti, Mengmeng Hao, Ardeshir Baktash, Lianzhou Wang

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  21. Machine Learning Prediction of Organic–Inorganic Halide Perovskite Solar Cell Performance from Optical Properties

    ACS Energy Letters · 2025-03-17

    Ruiqi Zhang, Brandon T. Motes, Shaun Wee Kiat Tan, Yongli Lu, Meng-Chen Shih, Yilun Hao, Karen Yang, Shreyas Srinivasan, Moungi G. Bawendi, Vladimir Bulović

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  22. Comparative analysis of machine learning techniques for temperature and humidity prediction in photovoltaic environments

    Scientific Reports · 2025-05-05

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  23. Unlocking a self-driving research workflow for perovskite photovoltaics

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    Lifang Xie, Yalan Zhang, Noah Peterkes, Xiao‐Fen Li, Yike Guo, Yuanyuan Zhou

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  24. 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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  25. Machine Learning Unlocks New Directions in Halide Perovskite Research

    ChemElectroChem · 2025-12-10

    Hyejin Choe, Heesung Yoon, Inhyang Kim, Soo Young Kim

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  26. AI-driven analysis of harmonic intensity in perovskite single crystals

    Journal of Materials Chemistry C · 2026-01-01

    Sarvani Jowhar Khanam, Harinath Kuruva, Srinivasa Rao Konda, Salman Abdul Moiz, Ramu Naidu Savu, Banavoth Murali

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  27. Efficient Discovery of Lead-Free A 3 BX 6 Halide Perovskites via Machine Learning

    ACS Sustainable Chemistry & Engineering · 2026-03-14

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  28. Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

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  29. High‐Accuracy Machine Learning Projections of Composition‐Dependent Thermal Stability in Halide Perovskites

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  30. Opposite and reversible optical switching in soft-lattice halide perovskites using light and heat

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