Systematic atomic structure datasets for machine learning potentials: 
Application to defects in magnesium

Poul, Marvin; Huber, Liam; Bitzek, Erik; Neugebauer, Jörg

doi:10.1103/PhysRevB.107.104103

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Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium

MPS-Authors

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Poul, Marvin
Thermodynamics and Kinetics of Defects, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Huber, Liam
Thermodynamics and Kinetics of Defects, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Bitzek, Erik
Department of Materials Science and Engineering, Institute i, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany;
Microstructure and Mechanics, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Neugebauer, Jörg
Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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PhysRevB.107.104103.pdf
(出版社版), 3MB

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引用

Poul, M., Huber, L., Bitzek, E., & Neugebauer, J. (2023). Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium. Physical Review B, 107:. doi:10.1103/PhysRevB.107.104103.

引用: https://hdl.handle.net/21.11116/0000-000E-7FC6-5

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