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Prediction of solar‐chargeable battery materials: A text‐mining and first‐principles investigation
Author(s) -
He Mu,
Zhang Lei
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6776
Subject(s) - battery (electricity) , process (computing) , lithium (medication) , computer science , lithium ion battery , energy storage , power (physics) , physics , medicine , quantum mechanics , endocrinology , operating system
Summary Photo‐rechargeable batteries utilize the solar energy to wirelessly charge the lithium‐ion batteries, which are feasible to realize more portable electric vehicles and electronic devices. However, the photo‐rechargeable battery materials are scarce and the search for the potential photo‐rechargeable battery materials that are capable of simultaneous light‐responsiveness and lithium‐ion storage is critical. In this manuscript, we employ a novel material discovery process combining the text‐mining and first‐principles investigation to identify and evaluate the potential photo‐battery material candidates via unsupervised learning of the materials science literature. The text‐mining process extracts useful information from the literature, detects possible materials reported in the papers that are previously unknown to the photo‐rechargeable battery application, and establishes the hidden relationships between the chemical names and their applicability to the photo‐rechargeable battery materials. New potential photo‐rechargeable battery materials are proposed. The present study highlights the importance of the text‐mining method for the energy conversion and storage applications, and provides a rational design strategy to develop novel energy materials.