Construction of Machine Learning Interatomic Potentials for Metals

Molecular dynamics (MD) is a powerful tool for modeling the phase and structural transformations and the evolution of defects and their influence on the metallic material properties. The accuracy of MD modeling directly depends on the quality of interatomic potentials. Modern machine-learning potent...

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Bibliographic Details
Published in:Russian physics journal 2024-09, Vol.67 (9), p.1408-1413
Main Authors: Dmitriev, S. V., Kistanov, A. A., Kosarev, I. V., Scherbinin, S. A., Shapeev, A. V.
Format: Article
Language:English
Subjects:
Condensed Matter Physics
Exact solutions
Hadrons
Heavy Ions
Lasers
Machine learning
Material properties
Mathematical and Computational Physics
Modelling
Molecular dynamics
Nuclear Physics
Optical Devices
Optics
Photonics
Physics
Physics and Astronomy
Theoretical
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Summary:Molecular dynamics (MD) is a powerful tool for modeling the phase and structural transformations and the evolution of defects and their influence on the metallic material properties. The accuracy of MD modeling directly depends on the quality of interatomic potentials. Modern machine-learning potentials are typically trained on random atomic configurations. This approach has significantly improved the quality of new potentials over traditional EAM potentials. In this work, exact solutions to the equations of atomic motion are offered to train the machine learning potentials.
ISSN:1064-8887
1573-9228
DOI:10.1007/s11182-024-03261-7