Anisotropic Thermal Transport in Chalcogenide Perovskite CaZrS3 from Machine Learning Interatomic Potential

Chalcogenide perovskites are being actively considered for photovoltaic, optoelectronic, and thermoelectric applications due to their high carrier mobility, strong light absorption, long-term stability, and environment-friendliness. For all these applications, thermal properties play a key role in determining the performance and lifetime of perovskite systems. In this work, we have developed a machine-learning Gaussian approximation potential to study the structural and thermal transport properties of chalcogenide perovskite CaZrS 3 . We show that the GAP achieves a DFT-level accuracy in describing both cubic and orthorhombic CaZrS 3 , with 2-4 orders of magnitude reduced computational cost. Specifically, we applied the GAP to predict the lattice thermal conductivities ( κ L ) and phonon properties of orthorhombic CaZrS 3 from 200 to 900 K by considering four-phonon processes. Compared to its counterpart CaZrSe 3 , the CaZrS 3 exhibits comparably low but relatively more anisotropic κ L mainly due to its strong anharmonicity and anisotropic group velocities. Specifically, its thermal conductivities along the a-and c-axis are close and notably lower than that along the b -axis. Optical phonons contribute as high as nearly half of the total thermal conductivity throughout the entire temperature range. Particularly, we observe non-*