Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/110709
Title: Predicting stable crystalline compounds using chemical similarity
Author(s): Wang, Hai-Chen
Botti, SilvanaLook up in the Integrated Authority File of the German National Library
Marques, MiguelLook up in the Integrated Authority File of the German National Library
Issue Date: 2021
Type: Article
Language: English
Abstract: We propose an efficient high-throughput scheme for the discovery of stable crystalline phases. Our approach is based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data-mining experimental databases. In this way we build 189,981 possible crystal phases, including 18,479 that are on the convex hull of stability. The 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 procedures. As a characterization of the set of 18,479 stable compounds, we calculate their electronic band gaps, magnetic moments, and hardness. Our 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.
URI: https://opendata.uni-halle.de//handle/1981185920/112664
http://dx.doi.org/10.25673/110709
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Journal Title: npj computational materials
Publisher: Nature Publ. Group
Publisher Place: London
Volume: 7
Original Publication: 10.1038/s41524-020-00481-6
Page Start: 1
Page End: 9
Appears in Collections:Open Access Publikationen der MLU

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