ArticleProceedings of the National Academy of Sciences of the United States of America2025
Global universal scaling and ultrasmall parameterization in machine-learning interatomic potentials with superlinearity.
Yanxiao Hu, Ye Sheng, Jing Huang, Xiaoxin Xu, Yuyan Yang, Mingqiang Zhang, Yabei Wu, Caichao Ye, Jiong Yang, Wenqing Zhang
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In one paragraphArticle in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
Yanxiao Hu *State Key Laboratory of Quantum Functional Materials and Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.ORCID 0000-0002-2790-7396 Ye Sheng *State Key Laboratory of Quantum Functional Materials and Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
Jing HuangState Key Laboratory of Quantum Functional Materials and Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
Xiaoxin XuState Key Laboratory of Quantum Functional Materials and Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
Yuyan YangMaterials Genome Institute, Shanghai Engineering Research Center for Integrated Circuits and Advanced Display Materials, Shanghai University, Shanghai 200444, China.
Mingqiang ZhangState Key Laboratory of Quantum Functional Materials and Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
Yabei WuState Key Laboratory of Quantum Functional Materials and Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
Caichao YeState Key Laboratory of Quantum Functional Materials and Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.ORCID 0000-0002-9197-8922 Jiong YangMaterials Genome Institute, Shanghai Engineering Research Center for Integrated Circuits and Advanced Display Materials, Shanghai University, Shanghai 200444, China.
Wenqing ZhangState Key Laboratory of Quantum Functional Materials and Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.ORCID 0000-0002-2551-6375 Funding
Guangdong Provincial Key Laboratory Of Computational Science And Material Design ( ) 2019B030301001MOST | National Key Research and Development Program of China (NKPs) 2022YFA1203400MOST | National Natural Science Foundation of China (NSFC) 92163212MOST | National Natural Science Foundation of China (NSFC) 92463310
6 · The paper itselfAbstract
Using machine learning (ML) to construct interatomic interactions and thus potential energy surface (PES) has become a common strategy for materials design and simulations. However, those current models of machine-learning interatomic potential (MLIP) consider no relevant physical constraints or global scaling and thus may owe intrinsic out-of-domain difficulty which underlies the challenges of model generalizability and physical scalability. Here, by incorporating the global universal scaling law, we develop an ultrasmall parameterized MLIP with superlinear expressive capability, named SUS
Indexed as
global scalinginteratomic potentialmachine learning
Identifiers
PMID40540595
PMCPMC12207430
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