Competition between phonon-vacancy and four-phonon scattering in cubic boron arsenide by machine learning interatomic potential

Point defects can strongly suppress the thermal conductivity $\ensuremath{\kappa}$ of solid materials, which is crucial for a broad range of applications, such as thermal management of electronic devices and thermoelectrics. Understanding thermal transport in materials containing point defects often relies on atomistic simulations based on density functional theory (DFT) or empirical potentials (EPs). However, modeling thermal transport in defective materials using DFT is very computationally expensive or even prohibitive due to the breaking of crystal symmetry while EPs suffer from low accuracy. Recently, machine learning has been applied to the development of interatomic potentials, offering opportunities to model defective systems accurately and efficiently. Here, we present a Gaussian approximation potential (GAP) developed for crystalline cubic boron arsenide (c-BAs) with vacancies, which can achieve DFT-level accuracy in predicting its $\ensuremath{\kappa}$ and phonon transport properties at four orders of magnitude reduced computational cost, especially for phonon-vacancy and four-phonon scatterings. Particularly, we applied the GAP to investigate the effect of vacancies on the $\ensuremath{\kappa}$ of c-BAs by considering both three-phonon and four-phonon scattering. Special attention was paid to the competition between phonon-vacancy and four-phonon scattering, which tend to decrease and increase the temperature dependence of $\ensuremath{\kappa}$, respectively. Specifically, when the vacancy concentration is much lower than $0.07%$ $(2.56\ifmmode\times\else\texttimes\fi{}{10}^{19}\phantom{\rule{4pt}{0ex}}{\mathrm{cm}}^{\ensuremath{-}3})$, four-phonon scattering plays stronger roles in determining the temperature dependence of $\ensuremath{\kappa}$. As the vacancy concentration increases to $0.07%$, the temperature dependence of $\ensuremath{\kappa}$ becomes close to that considering only three-phonon scattering, indicating the comparable effect of phonon-vacancy and four-phonon scattering. As the vacancy concentration further increases, the phonon-vacancy scattering becomes more dominant and pushes the $\ensuremath{\kappa}$ towards a temperature-independent behavior. Our work deepens the understanding of the phonon scattering landscape in c-BAs with vacancies and will be helpful for tailoring its thermal properties. Atomistic simulations combined with machine learning interatomic potentials are expected to be able to greatly advance the understanding of thermal transport in defective materials.