Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design
Lei Zhang
(Department of Materials Physics School of Chemistry and Materials Science Nanjing University of Information Science and Technology Nanjing 210044 P. R. China)
Yiru Huang
(Department of Materials Physics School of Chemistry and Materials Science Nanjing University of Information Science and Technology Nanjing 210044 P. R. China)
Leiming Yan
(Department of Materials Physics School of Chemistry and Materials Science Nanjing University of Information Science and Technology Nanjing 210044 P. R. China)
Jinghao Ge
(Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education; Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology International Joint Research Center of Shaanxi Province for Photoelectric Materials Science Institute for Advanced Energy Materials School of Materials Science and Engineering Shaanxi Normal University Xi'an Shaanxi 710119 P. R. China)
Xiaokang Ma
(State Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an Shaanxi 710072 P. R. China)
Zhike Liu
(Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education; Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology International Joint Research Center of Shaanxi Province for Photoelectric Materials Science Institute for Advanced Energy Materials School of Materials Science and Engineering Shaanxi Normal University Xi'an Shaanxi 710119 P. R. China)
Jiaxue You
(Department of Materials Science and Engineering Hong Kong Institute for Clean Energy (HKICE) City University of Hong Kong Kowloon Hong Kong SAR 999077 P. R. China)
Alex K. Y. Jen
(Department of Materials Science and Engineering Hong Kong Institute for Clean Energy (HKICE) City University of Hong Kong Kowloon Hong Kong SAR 999077 P. R. China)
Shengzhong Frank Liu
(Dalian National Laboratory for Clean Energy iChEM Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 P. R. China)
Journal: Advanced Intelligent Systems
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• Wiley
• ISSN: 2640-4567
Abstract
Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)‐based machine learning technique to build language models and automatically extract hidden information regarding perovskite solar cell (PSC) materials from 29 060 publications is employed. The concept that there are light‐absorbing materials, electron‐transporting materials, and hole‐transporting materials in PSCs is successfully learned by the NLP‐based machine learning model without a time‐consuming human expert training process. The NLP model highlights a hole‐transporting material that receives insufficient attention in the literature, which is then elaborated via density functional theory calculations to provide an atomistic view of the perovskite/hole‐transporting layer heterostructures and their optoelectronic properties. Finally, the above results are confirmed by device experiments. The present study demonstrates the viability of NLP as a universal machine learning tool to extract useful information from existing publications.
Lei Zhang, Yiru Huang, Leiming Yan, et al. (2024). "Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design". Advanced Intelligent Systems. https://doi.org/10.1002/aisy.202300678