Actively trained magnetic moment tensor potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN

We present a protocol for automated fitting of magnetic moment tensor potential explicitly including magnetic moments in its functional form. For the fitting of this potential we use energies, forces, stresses, and magnetic forces (negative derivatives of energies with respect to magnetic moments) of configurations selected with an active learning algorithm. These selected configurations are computed using constrained density functional theory, which enables calculating energies and their derivatives for both equilibrium and nonequilibrium (excited) magnetic states. We test our protocol on the system of B1-CrN and demonstrate that the automatically trained magnetic moment tensor potential reproduces mechanical, dynamical, and thermal properties of B1-CrN in the paramagnetic state with respect to density functional theory and experiments.

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Publication Details

Journal
Physical review. B./Physical review. B
Published
2025-03-27
DOI
https://doi.org/10.1103/physrevb.111.094438
Citations
5
Primary Topic
Boron and Carbon Nanomaterials Research
Type
article
Field-Weighted Citation Impact
2.59

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article

Actively trained magnetic moment tensor potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN

Alexander V. Shapeev, Ivan A. Kruglov, Christian Tantardini, Max Hodapp et al.
5 citations
Physical review. B./Physical review. B
Boron and Carbon Nanomaterials Research
2.59
article

Actively trained magnetic moment tensor potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN

Alexander V. Shapeev, Ivan A. Kruglov, Christian Tantardini, Max Hodapp, Alexey S. Kotykhov, Ivan S. Novikov, Konstantin Kravtsov
article en
5 citations

Abstract

We present a protocol for automated fitting of magnetic moment tensor potential explicitly including magnetic moments in its functional form. For the fitting of this potential we use energies, forces, stresses, and magnetic forces (negative derivatives of energies with respect to magnetic moments) of configurations selected with an active learning algorithm. These selected configurations are computed using constrained density functional theory, which enables calculating energies and their derivatives for both equilibrium and nonequilibrium (excited) magnetic states. We test our protocol on the system of B1-CrN and demonstrate that the automatically trained magnetic moment tensor potential reproduces mechanical, dynamical, and thermal properties of B1-CrN in the paramagnetic state with respect to density functional theory and experiments.

Physical review. B./Physical review. BVol. 111(9)
Skolkovo Institute of Science and Technology (RU), Moscow Institute of Physics and Technology (RU), Istituto Officina dei Materiali (IT), Materials Center Leoben (Austria) (AT), Institute of Biochemical Physics NM Emanuel (RU), Institute of Solid State Chemistry and Mechanochemistry (RU), Rice University (US)
Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie, Österreichische Forschungsförderungsgesellschaft, Russian Science Foundation
Affordable and clean energy
Openalex Percentile: Top 11%
Boron and Carbon Nanomaterials Research
2.59
DOISourceOpenAlex
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