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Finding new crystalline compounds using chemical similarity

Authors: H.-C. Wang, S. Botti, and M.A.L. Marques

Ref.: NPJ Comput. Mater. 7, 12 (2021)

Abstract: We propose an efficient high-throughput scheme for the discovery of new 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 189981 possible crystal phases, including 18479 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 first characterization of the set of 18479 new 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.

Citations: 6 (Google scholar)

DOI: 10.1038/s41524-020-00481-6

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Bibtex:

@article{Wang_2021,
	doi = {10.1038/s41524-020-00481-6},
	url = {https://doi.org/10.1038%2Fs41524-020-00481-6},
	year = 2021,
	month = {jan},
	publisher = {Springer Science and Business Media {LLC}},
	volume = {7},
	number = {1},
	author = {Hai-Chen Wang and Silvana Botti and Miguel A. L. Marques},
	title = {Predicting stable crystalline compounds using chemical similarity},
	journal = {npj Computational Materials}
}