Actively-trained magnetic Moment Tensor Potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN

TL;DR

This paper introduces an automated protocol for fitting magnetic Moment Tensor Potentials that incorporate magnetic moments, enabling accurate simulation of the properties of paramagnetic CrN across various states.

Contribution

The authors develop a novel automated fitting protocol for magnetic Moment Tensor Potentials that explicitly include magnetic moments, validated on CrN.

Findings

Accurately reproduces mechanical, dynamical, and thermal properties of CrN in the paramagnetic state.

Uses active learning with constrained DFT to select configurations for training.

Demonstrates agreement with density functional theory and experimental data.

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 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.