Han-Pu Liang

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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Mar 15, 2024 | dx.doi.org | Xun Xu |Han-Pu Liang |Zheng Liu |Qiu-Shi Huang

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Mar 15, 2024 | pubs.acs.org | Xun Xu |Han-Pu Liang |Qiu-Shi Huang |Zheng Liu

Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Am. Chem. Soc. 2024, XXXX, XXX Terms & Conditions Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses).

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.