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

Schmidt J, Chen L, Botti S, Marques MAL (2018)

Publication Type: Journal article

Publication year: 2018

Journal

Journal of Chemical Physics AIP Publishing

Book Volume: 148

Article Number: 241728

Journal Issue: 24

DOI: 10.1063/1.5020223

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. Our results suggest 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 of its predictive power still exist, in particular, for compounds involving the second-row of the periodic table or magnetic elements.

Involved external institutions

Martin-Luther-Universität Halle-Wittenberg (MLU) DE Germany (DE) Université Claude Bernard Lyon 1 (UCB) FR France (FR) Friedrich-Schiller-Universität Jena DE Germany (DE)

How to cite

APA:

Schmidt, J., Chen, L., Botti, S., & Marques, M.A.L. (2018). Predicting the stability of ternary intermetallics with density functional theory and machine learning. Journal of Chemical Physics, 148(24). https://doi.org/10.1063/1.5020223

MLA:

Schmidt, Jonathan, et al. "Predicting the stability of ternary intermetallics with density functional theory and machine learning." Journal of Chemical Physics 148.24 (2018).

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