Actively-trained magnetic Moment Tensor Potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915217282170880 |
|---|---|
| author | Kotykhov, Alexey S. Hodapp, Max Tantardini, Christian Kravtsov, Konstantin Kruglov, Ivan Shapeev, Alexander V. Novikov, Ivan S. |
| author_facet | Kotykhov, Alexey S. Hodapp, Max Tantardini, Christian Kravtsov, Konstantin Kruglov, Ivan Shapeev, Alexander V. Novikov, Ivan S. |
| contents | 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 non-equilibrium (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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20214 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Actively-trained magnetic Moment Tensor Potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN Kotykhov, Alexey S. Hodapp, Max Tantardini, Christian Kravtsov, Konstantin Kruglov, Ivan Shapeev, Alexander V. Novikov, Ivan S. Materials Science Atomic Physics 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 non-equilibrium (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. |
| title | Actively-trained magnetic Moment Tensor Potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN |
| topic | Materials Science Atomic Physics |
| url | https://arxiv.org/abs/2412.20214 |