On-the-fly machine learning-assisted high accuracy second-principles model for BaTiO3

Second-principles method is an efficient way to build atomistic models and is widely used to simulate various properties of perovskite ferroelectric materials. However, the state-of-the-art approach to constructing training set for second-principles model still highly relies on researcher’s experience and a universal approach remains elusive. In this work, we combine machine learning and second principles method to achieve automatic generation of second-principles model. The original training set is derived from phonons and is then updated based on the uncertainties predicted by machine learning with data generated via molecular dynamics simulations. This approach allows us to obtain a machine learning assisted second-principles model for BaTiO3, which has a much-improved accuracy compared to the model in our previous work [Physical Review B, 108 134117 (2023)]. Furthermore, we investigate thermal transport properties of BaTiO3 with the new second-principles model, and find a weak wave-like contribution to the thermal conductivity.