Predicting stable crystalline compounds using chemical similarity
作者机构：Institut für PhysikMartin-Luther-Universität Halle-Wittenberg06120 Halle(Saale)Germany Institut für Festkörpertheorie und-OptikFriedrich-Schiller-Universität Jena and European Theoretical Spectroscopy FacilityMax-Wien-Platz 107743 JenaGermany
出 版 物：《npj Computational Materials》 (计算材料学（英文）)
年 卷 期：2021年第7卷第1期
页 面：99-107页
核心收录：
学科分类：081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 070301[理学-无机化学] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：S.B.and M.A.L.M.acknowledge financial support from the DFG through Projects MA 6787/1-1 and BO 4280/8
主 题：stability similarity crystalline
摘 要：We propose an efficient high-throughput scheme for the discovery of stable crystalline *** approach is based on the transmutation of known compounds,through the substitution of atoms in the crystal structure with chemically similar *** concept of similarity is defined quantitatively using a measure of chemical replaceability,extracted by data-mining experimental *** this way we build 189,981 possible crystal phases,including 18,479 that are on the convex hull of *** resulting success rate of 9.72%is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials,and comparable with speed-up factors of machine learning filtering *** a characterization of the set of 18,479 stable compounds,we calculate their electronic band gaps,magnetic moments,and *** approach,that can be used as a filter on top of any high-throughput scheme,enables us to efficiently extract stable compounds from tremendously large initial sets,without any initial assumption on their crystal structures or chemical compositions.
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