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RESEARCH PAPER · APL Materials · 2020

Accelerated design of photovoltaic Ruddlesden–Popper perovskite Ca6Sn4S14−xOx using machine learning

Junjie Hu, Chenxi Wang, Qianhong Li, Rongjian Sa, Peng Fei Gao

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14 direct citing papers in this saved network. Explore the next layer with second-order citations.

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Accelerated design of photovoltaic Ruddlesden–Popper perovskite Ca6Sn4S14−xOx using machine learningOriginalMarked Near-Infrared Response of 2D Ca3Sn2S7Chalcogenide Perovskite via Solid and Electronic Structure Engineering11Gold-Based Double Perovskite-Related Polymorphs: Low Dimensional with an Ultranarrow Bandgap20Deep dive into machine learning density functional theory for materials science and chemistry111Silver–Bismuth Halide Double Salts for Lead‐Free Photovoltaics: Insights from Symmetry‐Based Modeling22Machine learning-driven design of promising perovskites for photovoltaic applications: A review18Predict the Polarizability and Order of Magnitude of Second Hyperpolarizability of Molecules by Machine Learning13Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells43Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit44A comprehensive review of machine learning applications in perovskite solar cells: Materials discovery, device performance, process optimization and systems integration41Harnessing the leading edge: machine learning ventures in chemistry and materials science3How to separate two Ts in a pod: Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning1Ruddlesden-Popper tolerance factor: An indicator predicting stability of 2D Ruddlesden-Popper phases17Dipole‐Moment‐Knowledge‐Guided Molecular Design for Perovskite Surface Passivation: A Gemma‐Language‐Model and DFT‐Driven Framework1

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14 direct citing papers saved.

  1. Marked Near-Infrared Response of 2D Ca3Sn2S7Chalcogenide Perovskite via Solid and Electronic Structure Engineering

    The Journal of Physical Chemistry C · 2021-09-13

    Lingyi Meng, Xiaoyan Yu, Juan Du, Junjie Shi, Can‐Zhong Lu, Peng Fei Gao

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  2. Gold-Based Double Perovskite-Related Polymorphs: Low Dimensional with an Ultranarrow Bandgap

    Chemistry of Materials · 2022-02-07

    Yuqi Fan, Qi Liu, Zilong Zhang, Shui‐Yang Lien, Yi Bing Xie, WanZhen Liang, Peng Fei Gao

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  3. Deep dive into machine learning density functional theory for materials science and chemistry

    Physical Review Materials · 2022-04-05

    Lenz Fiedler, Karan Shah, Michael Bußmann, Attila Cangi

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  4. Silver–Bismuth Halide Double Salts for Lead‐Free Photovoltaics: Insights from Symmetry‐Based Modeling

    Solar RRL · 2022-10-17

    Bruno Cucco, Laurent Pédesseau, Claudine Katan, Jacky Even, Mikaël Képénékian, George Volonakis

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  5. Machine learning-driven design of promising perovskites for photovoltaic applications: A review

    Surfaces and Interfaces · 2022-11-04

    Jinlian Chen, Mengjia Feng, Chenyang Zha, Cairu Shao, Linghai Zhang, Lin Wang

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  6. Predict the Polarizability and Order of Magnitude of Second Hyperpolarizability of Molecules by Machine Learning

    The Journal of Physical Chemistry A · 2023-07-14

    Guoxiang Zhao, Weiyin Yan, Zirui Wang, Yao Kang, Zuju Ma, Zhi‐Gang Gu, Qiaohong Li, Jian Zhang

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  7. Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells

    Advanced Energy Materials · 2023-07-30

    Nastaran Meftahi, Maciej Adam Surmiak, ‪Sebastian O. Fürer, Kevin J. Rietwyk, Jianfeng Lu, Sonia R. Raga, C.C. Evans, Monika Michalska, Hao Deng, David P. McMeekin, Tuncay Alan, Dechan Angmo, Doojin Vak, Anthony Chesman, Andrew Joseph Christofferson, David A. Winkler, Udo Bach, Salvy P. Russo

    Read original paper ↗ ·

  8. Machine‐Learning Accelerating the Development of Perovskite Photovoltaics

    Solar RRL · 2023-09-26

    Tiantian Liu, Sen Wang, Yinguang Shi, Lei Wu, Ruiyu Zhu, Yong Wang, Jun Zhou, Wallace C. H. Choy

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  9. High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit

    Nature Communications · 2024-02-05

    Xuan-Kun Li, Jian-Xu Ma, Xiangyu Li, Junjie Hu, Chuan-Yang Ding, Feng-Kai Han, Xiaomin Guo, X. Gary Tan, Xian‐Min Jin

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  10. 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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  11. Harnessing the leading edge: machine learning ventures in chemistry and materials science

    Physical Chemistry Chemical Physics · 2025-01-01

    Yuheng Li, Fengming Guo, Shui‐Yang Lien, Abd. Rashid bin Mohd Yusoff, Zhihong Zheng, Jingyun Zhang, Peng Fei Gao

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  12. How to separate two Ts in a pod: Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning

    Journal of Solid State Chemistry · 2025-02-07

    Dmitry Vrublevskiy, Loïc Robert, Balaranjan Selvaratnam, Arthur Mar

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  13. Ruddlesden-Popper tolerance factor: An indicator predicting stability of 2D Ruddlesden-Popper phases

    Acta Materialia · 2025-04-02

    Hyo Gyeong Shin, Eun Ho Kim, Jaeseon Kim, H.T. Kim, Donghwa Lee

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  14. Dipole‐Moment‐Knowledge‐Guided Molecular Design for Perovskite Surface Passivation: A Gemma‐Language‐Model and DFT‐Driven Framework

    Advanced Theory and Simulations · 2025-11-20

    Tianhui Jiang, Yifeng Gao, Guozhen Liu, Guoxiang Zhao, Junjie Hu, Rongjian Sa, Peng Fei Gao

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