Chuan-Nan Li

Mar 25, 2024 | pubs.acs.org | Han-Pu Liang |Chuan-Nan Li |Ran Zhou |Xun Xu

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Mar 25, 2024 | dx.doi.org | Han-Pu Liang |Chuan-Nan Li |Ran Zhou |Xun Xu

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Sep 30, 2023 | nature.com | Han-Pu Liang |Xie Zhang |Zijing Lin |Chuan-Nan Li

AbstractStructural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, which includes a symmetry-based combinatorial crystal optimization program (SCCOP) and a feature additive attribution model, to significantly reduce computational costs and to extract property-related structural features.