HH130: a standardized database of machine learning interatomic potentials, datasets, and its applications in the thermal transport of half-Heusler thermoelectrics

Abstract

High-throughput screening of thermoelectric materials from databases requires efficient and accurate computational methods. Machine-learning interatomic potentials (MLIPs) provide a promising avenue, facilitating the development of database-driven thermal transport applications through high-throughput simulations. However, the present challenge is the lack of standardized databases and openly available models for precise large-scale simulations. Here, we introduce HH130, a standardized database for 130 half-Heusler (HH) compounds in MatHub-3d (http://www.mathub3d.net), containing both MLIP models and datasets for the thermal transport of HH thermoelectrics. HH130 contains 31 891 total configurations (∼245 configurations per HH) and 390 MLIP models (three models per HH), generated using the dual adaptive sampling method to cover a wide range of thermodynamic conditions, and can be openly accessed on MatHub-3d. Comprehensive validation against first-principles calculations demonstrates that the MLIP models accurately predict energies, forces, and interatomic force constants (IFCs). The MLIP models in HH130 enabled us to efficiently perform four-phonon interactions for 80 HHs with phonon frequencies closely matching ab initio results. It is found that HHs with an 8 valence electron count (VEC) per unit cell generally exhibit lower lattice thermal conductivities (κ_Ls) compared to those with an 18 VEC, due to a combination of low 2nd-order IFCs and large scattering phase spaces in the former group. Additionally, we identified several HHs that demonstrate significant reductions in κ_L due to four-phonon interactions. HH130 provides a robust platform for high-throughput computation of κ_L and aids in the discovery of next-generation thermoelectrics through machine learning.