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Herein, the natural language processing (NLP)\u2010based machine learning technique to build language models and automatically extract hidden information regarding perovskite solar cell (PSC) materials from 29\u2009060 publications is employed. The concept that there are light\u2010absorbing materials, electron\u2010transporting materials, and hole\u2010transporting materials in PSCs is successfully learned by the NLP\u2010based machine learning model without a time\u2010consuming human expert training process. The NLP model highlights a hole\u2010transporting 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\u2010transporting 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.<\/jats:p>","DOI":"10.1002\/aisy.202300678","type":"journal-article","created":{"date-parts":[[2024,5,12]],"date-time":"2024-05-12T15:28:49Z","timestamp":1715527729000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design"],"prefix":"10.1002","volume":"6","author":[{"given":"Lei","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Materials Physics School of Chemistry and Materials Science Nanjing University of Information Science and Technology  Nanjing 210044 P. R. 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