Machine learning for thermal transport and phonon high-order anharmonicity in high thermal conductivity materials: A case study in boron arsenide

Materials with high thermal conductivity are at the forefront of research in advancing thermal management, as benchmarked by the recent discovery of cubic BAs. In this study, we utilized BAs as a prototype material to assess the predictive capabilities of a machine learning approach for thermal transport, particularly in scenarios where high-order phonon anharmonicity plays a crucial role. We developed a training methodology for the moment tensor potential based on ab initio molecular dynamics, which provides accurate predictions of atomic energies, forces, stresses, phonon dispersion relations, elastic modulus, and thermal expansion coefficients. Our approach yields quantitative predictions of thermal conductivity and phonon mean free paths, closely matching first-principles calculations and experimental measurements under varied conditions and size confinements. The predictions of pressure-dependent thermal conductivity, taking into account complex interactions from phonon anharmonicity, isotope scattering, and defect scattering, reveal intrinsic behavior resulting from competing 3-phonon and 4-phonon processes in high-quality BAs, while also showing weak pressure dependence in samples dominated by defects. This study explores the feasibility of using machine learning for simulating high-order phonon scattering and demonstrates its potential as a high-throughput computational approach in advancing thermal management solutions.