Training of Machine Learning Potentials for the Modeling of Nucleation in Graphite

The parameterization of machine learning potentials (MLP) for precise characterization of the interaction between carbon atoms in graphite and diamond phases is described. The training set consisted of various allotropic forms of carbon and their compounds. The MLPs are trained using forces, energie...

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Bibliographic Details
Published in:Journal of structural chemistry 2024-04, Vol.65 (4), p.831-839
Main Authors: Erokhin, S. V., Builova, M. A., Sorokin, P. B.
Format: Article
Language:English
Subjects:
Atomic
Atomic/Molecular Structure and Spectra
Carbon
Chemistry
Chemistry and Materials Science
Diamond tools
Elastic properties
Graphite
Inorganic Chemistry
Machine learning
Molecular
Nucleation
Optical and Plasma Physics
Parameterization
Physical Chemistry
Solid State Physics
Stress tensors
Tensors
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Description
Summary:The parameterization of machine learning potentials (MLP) for precise characterization of the interaction between carbon atoms in graphite and diamond phases is described. The training set consisted of various allotropic forms of carbon and their compounds. The MLPs are trained using forces, energies, and stress tensors obtained from ab initio simulations. It is shown that the MLPs can accurately reproduce elastic properties and structural parameters of carbon phases. However, the MLPs also predict some unphysical behavior due to the training set limitations and the lack of long-range interactions in the MLPs. In spite of these limitations, MLPs are a promising tool for the accurate characterization of diamond nucleation in graphite.
ISSN:0022-4766
1573-8779
DOI:10.1134/S0022476624040188