CUED Publications database

Machine-learned interatomic potentials for alloys and alloy phase diagrams

Rosenbrock, CW and Gubaev, K and Shapeev, AV and Pártay, LB and Bernstein, N and Csányi, G and Hart, GLW (2021) Machine-learned interatomic potentials for alloys and alloy phase diagrams. npj Computational Materials, 7.

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© 2021, The Author(s). We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTPs) are polynomial-like functions of interatomic distances and angles. The Gaussian approximation potential (GAP) framework uses kernel regression, and we use the smooth overlap of atomic position (SOAP) representation of atomic neighborhoods that consist of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbor density. Both types of potentials give excellent accuracy for a wide range of compositions, competitive with the accuracy of cluster expansion, a benchmark for this system. While both models are able to describe small deformations away from the lattice positions, SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations, and MTP allows, due to its lower computational cost, the calculation of compositional phase diagrams. Given the fact that both methods perform nearly as well as cluster expansion but yield off-lattice models, we expect them to open new avenues in computational materials modeling for alloys.

Item Type: Article
Uncontrolled Keywords: cond-mat.mtrl-sci cond-mat.mtrl-sci physics.comp-ph
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 30 Jul 2019 02:06
Last Modified: 04 Mar 2021 04:02
DOI: 10.1038/s41524-020-00477-2