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RESEARCH PAPER · Solar RRL · 2023

Interpretable Machine Learning Model for the Highly Accurate Prediction of Efficiency of Ternary Organic Solar Cells Based on Nonfullerene Acceptor using Effective Molecular Descriptors

Min‐Hsuan Lee

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

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Interpretable Machine Learning Model for the Highly Accurate Prediction of Efficiency of Ternary Organic Solar Cells Based on Nonfullerene Acceptor using Effective Molecular DescriptorsOriginalFrontier Molecular Orbital Offset as an Empirical Descriptor for Predicting Short Circuit Current of Nonfullerene Organic Solar Cells12A present scenario of the computational approaches for ternary organic solar cells3Machine learning study of D:A1:A2 ternary organic solar cells17Extrapolative Machine Learning for Accurate Efficiency Prediction in Non‐Fullerene Ternary Organic Solar Cells: Leveraging Computable Molecular Descriptors in High‐Throughput Virtual Screening10Advanced High‐Throughput Rational Design of Porphyrin‐Sensitized Solar Cells Using Interpretable Machine Learning7Intrinsic Reactivity Index as Descriptor for Accurately Predicting Short-Circuit Current Density in Ternary Organic Solar Cells6PEDOT:PSS treated Xuan paper as a green electronics material for wearable dry electrocardiogram electrodes and flexible strain sensors13Machine learning theory for acceptor molecular design of D:A1:A2 ternary organic solar cells3General machine learning models for interpreting and predicting efficiency degradation in organic solar cells8High-Throughput Molecular Design of Donors and Non-Fullerene Acceptors for Organic Solar Cells Based on Convolutional Neural Networks12Waste‐to‐Wearables: Stretchable Sensors from Discarded Mask Straps for Real‐Time Motion Detection3Accurate and expandable graph deep learning for accelerating predictive identification of high-potential organic photovoltaics1Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding0

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  1. Frontier Molecular Orbital Offset as an Empirical Descriptor for Predicting Short Circuit Current of Nonfullerene Organic Solar Cells

    Solar RRL · 2023-07-18

    Min‐Hsuan Lee

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  2. A present scenario of the computational approaches for ternary organic solar cells

    Journal of Renewable and Sustainable Energy · 2023-11-01

    Oscar Eraso, Daniela Bolaños, Nikolas Echeverri, Carolina Orozco Donneys, Tayebeh Ameri, José Darío Perea

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  3. Machine learning study of D:A1:A2 ternary organic solar cells

    Organic Electronics · 2023-12-29

    Jinhong Li, Cai‐Rong Zhang, Meiling Zhang, Xiaomeng Liu, Ji-Jun Gong, Yuhong Chen, Zi‐Jiang Liu, Youzhi Wu, Hongshan Chen

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  4. Extrapolative Machine Learning for Accurate Efficiency Prediction in Non‐Fullerene Ternary Organic Solar Cells: Leveraging Computable Molecular Descriptors in High‐Throughput Virtual Screening

    Solar RRL · 2024-05-28

    Jian‐Ming Liao, Hui‐Hsu Gavin Tsai

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  5. Advanced High‐Throughput Rational Design of Porphyrin‐Sensitized Solar Cells Using Interpretable Machine Learning

    Advanced Science · 2024-09-24

    Jian‐Ming Liao, Yu‐Hsuan Chen, Hsuan‐Wei Lee, Bo‐Cheng Guo, Po‐Cheng Su, Lun‐Hong Wang, Nagannagari Masi Reddy, Aswani Yella, Zhao‐Jie Zhang, Chuan‐Yung Chang, Chia‐Yuan Chen, Shaik Mohammed Zakeeruddin, Hui‐Hsu Gavin Tsai, Chen‐Yu Yeh, Michaël Grätzel

    Read original paper ↗ ·

  6. Intrinsic Reactivity Index as Descriptor for Accurately Predicting Short-Circuit Current Density in Ternary Organic Solar Cells

    ACS Applied Energy Materials · 2025-01-01

    Min‐Hsuan Lee, Yu‐Tang Hsiao, Chuang-Yi Liao, Yi‐Ming Chang

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  7. PEDOT:PSS treated Xuan paper as a green electronics material for wearable dry electrocardiogram electrodes and flexible strain sensors

    Journal of environmental chemical engineering · 2025-02-05

    Teng Kuan-Hsiang, Min‐Hsuan Lee

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  8. Machine learning theory for acceptor molecular design of D:A1:A2 ternary organic solar cells

    Physics Letters A · 2025-06-21

    Jinhong Li, Cai‐Rong Zhang, Ji-Jun Gong, Xiaomeng Liu, Zi‐Jiang Liu, Yuhong Chen, Youzhi Wu, Hongshan Chen

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  9. General machine learning models for interpreting and predicting efficiency degradation in organic solar cells

    Expert Systems with Applications · 2025-07-16

    David Valiente, Fernando Rodríguez‐Mas, Juan V. Alegre‐Requena, David Dalmau, María Flores, Juan Carlos Ferrer

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  10. High-Throughput Molecular Design of Donors and Non-Fullerene Acceptors for Organic Solar Cells Based on Convolutional Neural Networks

    Journal of Chemical Information and Modeling · 2025-09-16

    L.T. Chen, Cai‐Rong Zhang, Cuicui Sang, Xiaomeng Liu, Ji-Jun Gong, Meiling Zhang, Hongshan Chen

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  11. Waste‐to‐Wearables: Stretchable Sensors from Discarded Mask Straps for Real‐Time Motion Detection

    Advanced Materials Technologies · 2025-11-05

    Ya‐Yu Liang, Nien‐Tsu Wu, Min‐Hsuan Lee

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  12. Accurate and expandable graph deep learning for accelerating predictive identification of high-potential organic photovoltaics

    Chemical Engineering Journal · 2026-04-03

    Chuan-Yung Chang, Yi-Ling Wu, Yu-Chen Huang, Jhih-Jheng Ke, Hui‐Hsu Gavin Tsai

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  13. Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding

    ACS Omega · 2026-05-11

    Bibhas Das, Anirban Mondal

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