Machine-learned interatomic potentials for alloys and alloy phase diagrams

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 approximat...

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Autores principales:	Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev, Livia B. Pártay, Noam Bernstein, Gábor Csányi, Gus L. W. Hart
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/47b2072bae1e4a3da976b0281a49afb1
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spelling	oai:doaj.org-article:47b2072bae1e4a3da976b0281a49afb12021-12-02T13:57:35ZMachine-learned interatomic potentials for alloys and alloy phase diagrams10.1038/s41524-020-00477-22057-3960https://doaj.org/article/47b2072bae1e4a3da976b0281a49afb12021-01-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00477-2https://doaj.org/toc/2057-3960Abstract 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.Conrad W. RosenbrockKonstantin GubaevAlexander V. ShapeevLivia B. PártayNoam BernsteinGábor CsányiGus L. W. HartNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Conrad W. Rosenbrock Konstantin Gubaev Alexander V. Shapeev Livia B. Pártay Noam Bernstein Gábor Csányi Gus L. W. Hart Machine-learned interatomic potentials for alloys and alloy phase diagrams
description	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.
format	article
author	Conrad W. Rosenbrock Konstantin Gubaev Alexander V. Shapeev Livia B. Pártay Noam Bernstein Gábor Csányi Gus L. W. Hart
author_facet	Conrad W. Rosenbrock Konstantin Gubaev Alexander V. Shapeev Livia B. Pártay Noam Bernstein Gábor Csányi Gus L. W. Hart
author_sort	Conrad W. Rosenbrock
title	Machine-learned interatomic potentials for alloys and alloy phase diagrams
title_short	Machine-learned interatomic potentials for alloys and alloy phase diagrams
title_full	Machine-learned interatomic potentials for alloys and alloy phase diagrams
title_fullStr	Machine-learned interatomic potentials for alloys and alloy phase diagrams
title_full_unstemmed	Machine-learned interatomic potentials for alloys and alloy phase diagrams
title_sort	machine-learned interatomic potentials for alloys and alloy phase diagrams
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/47b2072bae1e4a3da976b0281a49afb1
work_keys_str_mv	AT conradwrosenbrock machinelearnedinteratomicpotentialsforalloysandalloyphasediagrams AT konstantingubaev machinelearnedinteratomicpotentialsforalloysandalloyphasediagrams AT alexandervshapeev machinelearnedinteratomicpotentialsforalloysandalloyphasediagrams AT liviabpartay machinelearnedinteratomicpotentialsforalloysandalloyphasediagrams AT noambernstein machinelearnedinteratomicpotentialsforalloysandalloyphasediagrams AT gaborcsanyi machinelearnedinteratomicpotentialsforalloysandalloyphasediagrams AT guslwhart machinelearnedinteratomicpotentialsforalloysandalloyphasediagrams
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Machine-learned interatomic potentials for alloys and alloy phase diagrams

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