Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential.

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Bibliographic Details
Title: Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential.
Authors: Sivak JT; The Pennsylvania State University, Department of Chemistry, University Park, Pennsylvania 16802, USA., Almishal SSI; The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania 16802, USA., Caucci MK; The Pennsylvania State University, Department of Chemistry, University Park, Pennsylvania 16802, USA., Tan Y; The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania 16802, USA., Srikanth D; The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania 16802, USA., Petruska J; The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania 16802, USA., Furst M; The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania 16802, USA., Chen LQ; The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania 16802, USA.; The Pennsylvania State University, Department of Mathematics, University Park, Pennsylvania 16802, USA.; The Pennsylvania State University, Department of Engineering Science and Mechanics, University Park, Pennsylvania 16802, USA., Rost CM; Virginia Polytechnic Institute and State University, Department of Materials Science and Engineering, Blacksburg, Virginia 24061, USA., Maria JP; The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania 16802, USA., Sinnott SB; The Pennsylvania State University, Department of Chemistry, University Park, Pennsylvania 16802, USA.; The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania 16802, USA.; The Pennsylvania State University, Institute for Computational and Data Sciences, University Park, Pennsylvania 16802, USA.
Source: Physical review letters [Phys Rev Lett] 2025 May 30; Vol. 134 (21), pp. 216101.
Publication Type: Journal Article
Journal Info: Publisher: American Physical Society Country of Publication: United States NLM ID: 0401141 Publication Model: Print Cited Medium: Internet ISSN: 1079-7114 (Electronic) Linking ISSN: 00319007 NLM ISO Abbreviation: Phys Rev Lett Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1079-7114
DOI:10.1103/PhysRevLett.134.216101