Details

Autor(en) / Beteiligte

• Rosenbrock, Conrad W.
• Gubaev, Konstantin
• Shapeev, Alexander V.
• Pártay, Livia B.
• Bernstein, Noam
• Csányi, Gábor
• Hart, Gus L. W.

Titel

Machine-learned interatomic potentials for alloys and alloy phase diagrams

Ist Teil von

• npj computational materials, 2021-01, Vol.7 (1), p.1-9, Article 24

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Abstract 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.