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RESEARCH PAPER · Chemistry of Materials · 2022

Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data

Yuta Miyake, Kakaraparthi Kranthiraja, Fumitaka Ishiwari, Akinori Saeki

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Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure DataOriginalOpportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells25Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design5Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors51π-Conjugated Materials: Here, There, and Everywhere60Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline22Impact of Alkyl-Based Side Chains in Conjugated Materials for Bulk Heterojunction Organic Photovoltaic Cells—A Review13Data‐Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top‐Down/Bottom‐Up Design30A Comparative Review: Research in Safety and Sustainability of Carbon Nanomaterials Without and With Machine Learning Assistance11Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices28Effectiveness and limitation of the performance prediction of perovskite solar cells by process informatics11Machine Learning and Small Data-Guided Optimization of Silica Shell Morphology on Gold Nanorods10Predictive Modeling and Design of Organic Solar Cells: A Data-Driven Approach for Material Innovation19Comparative analysis of search approaches to discover donor molecules for organic solar cells0Design of simple-structured conjugated polymers for organic solar cells by machine learning-assisted structural modification and experimental validation2Exploring the chemistry of higher acenes: from synthesis to applications20Recent developments in polymer semiconductors with excellent electron transport performances58AI-Driven Inverse Design of Materials: Past, Present, and Future78Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation48Strategies and Mechanisms To Minimize Energy Loss in Non-Fullerene Bulk Heterojunction Organic Solar Cells: Experimental and Computational Approaches11Investigation of Short‐Circuit Current Density in Non‐Fullerene‐Based Ternary Organic Solar Cells by Incorporating Machine Learning Algorithms with Effective Descriptors4Machine learning in polymer science: A new lens for physical and chemical exploration35From fragments to function: data-driven design of high-performance non-fullerene acceptors for organic photovoltaics2Descriptor Engineering for Machine-Learning-Based Performance Prediction in Organic Solar Cells: A Mini Review2Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding0Physics-Aware Learning of Donor–Acceptor Pair Interactions for High-Efficiency Organic Photovoltaics1Prediction of High-Performance Donor–Acceptor Pairs for Organic Photovoltaics with Machine Learning0Machine-Learning Prediction of Carrier Mobility and Polarity Type of Polymer Semiconductors: from High-Fidelity Data to Experimental Realization0

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  1. Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells

    Journal of Materials Chemistry C · 2022-01-01

    Prateek Malhotra, Kanupriya Khandelwal, Subhayan Biswas, Fang‐Chung Chen, Ganesh Datt Sharma

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  2. Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design

    ChemRxiv · 2022-12-05

    J. Terence Blaskovits, Rubén Laplaza, Sergi Vela, Clémence Corminbœuf

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  3. Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors

    Journal of Materials Chemistry A · 2023-01-01

    Rakesh Suthar, T. Abhijith, Supravat Karak

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  4. π-Conjugated Materials: Here, There, and Everywhere

    Chemistry of Materials · 2023-02-28

    Aurelio Mateo‐Alonso

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  5. Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline

    International Journal of Quantum Chemistry · 2023-08-25

    Khadijah Mohammedsaleh Katubi, Muhammad Saqib, Tayyaba Mubashir, Mudassir Hussain Tahir, Mohamed Ibrahim Halawa, Alveena Akbar, Beriham Ibrahim Basha, Muhammad Sulaman, Ziyad Awadh Alrowaili, Mohammed Sultan Al-Buriahi

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  6. Impact of Alkyl-Based Side Chains in Conjugated Materials for Bulk Heterojunction Organic Photovoltaic Cells—A Review

    Energies · 2023-09-15

    Kathleen I. Chane‐Ching

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  7. Data‐Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top‐Down/Bottom‐Up Design

    Advanced Materials · 2023-10-10

    J. Terence Blaskovits, Rubén Laplaza, Sergi Vela, Clémence Corminbœuf

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  8. A Comparative Review: Research in Safety and Sustainability of Carbon Nanomaterials Without and With Machine Learning Assistance

    IEEE Access · 2024-01-01

    Liqing Wang, Hongyan Wang, Mingyu Bai, Yin Wu, Tongshu Guo, Dirui Cai, Peiyan Sun, Na Xiao, Ansheng Li, Wuyi Ming

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  9. Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices

    Journal of Materials Chemistry A · 2024-01-01

    Martin T. Seifrid, Stanley Lo, Dylan G. Choi, Gary Tom, My Linh Le, Kunyu Li, Rahul Sankar, Hoai‐Thanh Vuong, Hiba Wakidi, Ahra Yi, Ziyue Zhu, Nora Schopp, Aaron Peng, Benjamin R. Luginbuhl, Thuc‐Quyen Nguyen, Alán Aspuru‐Guzik

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  10. Effectiveness and limitation of the performance prediction of perovskite solar cells by process informatics

    Energy Advances · 2024-01-01

    Ryo Fukasawa, Toru Asahi, Takuya Taniguchi

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  11. Machine Learning and Small Data-Guided Optimization of Silica Shell Morphology on Gold Nanorods

    Chemistry of Materials · 2024-08-22

    Akhlak U. Mahmood, Melanie M. Ghelardini, Joseph B. Tracy, Yaroslava G. Yingling

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  12. Predictive Modeling and Design of Organic Solar Cells: A Data-Driven Approach for Material Innovation

    ACS Applied Energy Materials · 2024-10-07

    Bibhas Das, Anirban Mondal

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  13. Comparative analysis of search approaches to discover donor molecules for organic solar cells

    Digital Discovery · 2025-01-01

    Mohammed Azzouzi, Steven Bennett, Víctor Posligua, Roberto Bondesan, Martijn A. Zwijnenburg, Kim E. Jelfs

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  14. Design of simple-structured conjugated polymers for organic solar cells by machine learning-assisted structural modification and experimental validation

    Digital Discovery · 2025-01-01

    Shogo Tadokoro, Ryosuke Kamimura, Fumitaka Ishiwari, Akinori Saeki

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  15. Exploring the chemistry of higher acenes: from synthesis to applications

    Chemical Science · 2025-01-01

    Hironobu Hayashi, Hiroko Yamada

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  16. Recent developments in polymer semiconductors with excellent electron transport performances

    Chemical Society Reviews · 2025-01-01

    Yunchao Zhang, Weifeng Zhang, Zhihui Chen, Liping Wang, Gui Yu

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  17. AI-Driven Inverse Design of Materials: Past, Present, and Future

    Chinese Physics Letters · 2025-01-31

    Xu Han, Xin-De Wang, Meng-Yuan 孟圆 Xu 徐, Feng Zhen, Bo-Wen 博文 Yao 姚, Peng-Jie Guo, Ze-Feng Gao, Zhong-Yi Lu

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  18. Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation

    Solar Energy · 2025-03-05

    Hussein Togun, Ali Basem, Muhsin J. Jweeg, Nirmalendu Biswas, Azher M. Abed, Dipankar Paul, Hayder Ibrahim Mohammed, A.K. Chattopadhyay, Bhupendra Kumar Sharma, Tuqa Abdulrazzaq

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  19. Strategies and Mechanisms To Minimize Energy Loss in Non-Fullerene Bulk Heterojunction Organic Solar Cells: Experimental and Computational Approaches

    Energy & Fuels · 2025-03-06

    Hemraj Dahiya, Rakesh Suthar, Supravat Karak, Ganesh Datt Sharma

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  20. Investigation of Short‐Circuit Current Density in Non‐Fullerene‐Based Ternary Organic Solar Cells by Incorporating Machine Learning Algorithms with Effective Descriptors

    Solar RRL · 2025-05-01

    Min‐Hsuan Lee, Ying‐Chun Chen, Yi‐Ming Chang, Bo Hou

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  21. Machine learning in polymer science: A new lens for physical and chemical exploration

    Progress in Materials Science · 2025-07-29

    Xiao-Qin Cao, Yongqing Zhang, Zhenghua Sun, Hongyao Yin, Yujun Feng

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  22. From fragments to function: data-driven design of high-performance non-fullerene acceptors for organic photovoltaics

    Molecular Systems Design & Engineering · 2025-12-08

    Bibhas Das, Kalyani Patrikar, Atharva Sachin Keny, Anirban Mondal

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  23. Descriptor Engineering for Machine-Learning-Based Performance Prediction in Organic Solar Cells: A Mini Review

    ACS Applied Electronic Materials · 2026-01-29

    Haibo Mao, Hong Zhang, Yuan Zhang, Cristiano Francisco Woellner, Huiqiong Zhou

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  24. 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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  25. Physics-Aware Learning of Donor–Acceptor Pair Interactions for High-Efficiency Organic Photovoltaics

    Macromolecules · 2026-05-25

    Yurun Lyu, Haoke Qiu, Zhong‐Yuan Lu, Z X Sun

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  26. Prediction of High-Performance Donor–Acceptor Pairs for Organic Photovoltaics with Machine Learning

    Solar · 2026-08-17

    Esther Mbina, Bruno Grandidier, Kekeli N’Konou

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  27. Machine-Learning Prediction of Carrier Mobility and Polarity Type of Polymer Semiconductors: from High-Fidelity Data to Experimental Realization

    Journal of the American Chemical Society · 2026-09-18

    Yuan‐Kai Li, Si-Lu Li, Yi Liu, Haotian Wu, Tian-Yu Zhang, Jie‐Yu Wang, Ze‐Fan Yao, Jian Pei

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