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Predicting the stability of ternary intermetallics with density functional theory and machine learning

Authors: J. Schmidt, L. Chen, S. Botti, and M.A.L. Marques

Ref.: J. Chem. Phys. 148, 241728 (2018)

Abstract: We use a combination of machine learning techniques and high-throughput density-functional theory calculations to explore ternary compounds with the AB2C2 composition. We chose the two most common intermetallic prototypes for this composition, namely the tI10-CeAl2Ga2 and the tP10-FeMo2B2 structures. We find that there may be ~10 times more stable compounds in these phases than previously known. These are mostly metallic and non-magnetic. While the use of machine learning reduces the overall calculation cost by around 75%, some limitations still exist, in particular for compounds involving the second-row of the periodic table or magnetic elements.

Citations: 8 (Google scholar)

DOI: 10.1063/1.5020223

Bibtex:

@article{Schmidt_2018,
	doi = {10.1063/1.5020223},
	url = {https://doi.org/10.1063%2F1.5020223},
	year = 2018,
	month = {jun},
	publisher = {{AIP} Publishing},
	volume = {148},
	number = {24},
	pages = {241728},
	author = {Jonathan Schmidt and Liming Chen and Silvana Botti and Miguel A. L. Marques},
	title = {Predicting the stability of ternary intermetallics with density functional theory and machine learning},
	journal = {The Journal of Chemical Physics}
}