Using Machine-Learned Force Fields for Describing Heat-Transport-Related Quantities in AlGaN and Derived Materials
Abstract
In this work, we develop machine-learned moment tensor potentials (MTPs) to simulate the static and dynamic structural properties in AlxGa1−xN and related materials. The potentials are trained on DFT-calculated data for forces, stresses, and energies obtained from random atomic displacements and cell deformations. MTP-calculated physical properties, including lattice parameters and elastic constants, thermal expansion, harmonic and anharmonic vibrational properties, and the thermal conductivity, are benchmarked against first-principles results and experimental data. The comparisons testify to the very high accuracy achieved by the machine-learned potentials despite the massively reduced computational effort. Additionally, the impact of various aspects of the MTP training procedure is examined.
Fields of Study & Subjects
Machine Learning in Materials Science
Thermal properties of materials
GaN-based semiconductor devices and materials
Publication details
- Journal / source
- Condensed Matter
- Publisher
- MDPI AG
- Volume
- 11
- Issue
- 2
- Pages
- 23–23
- Publication date
- 11 June 2026
- Language
- EN