Machine learning potentials of kaolinite based on the potential energy surfaces of GGA and meta-GGA density functional theory

Abstract

Machine learning molecular dynamics (MLMD) is a promising method for predicting material properties with high accuracy and low computational costs. Two kaolinite machine learning potentials (MLPs) were created based on a dataset that consists of the results of the first-principles calculations with the Perdew–Burke–Ernzerhof (PBE) generalized gradient approximation (GGA) and the strongly constrained and appropriately normed (SCAN) mata-GGA functionals. The structural and mechanical properties of kaolinite were evaluated by the MLMD simulations with the MLPs. The results were compared with those obtained by the density functional theory simulations, classical molecular dynamics simulations, and experiments. The MLMD with the MLP based on SCAN performed well for an accurate evaluation of these kaolinite properties. The vibrational density of states of kaolinite was evaluated using the MLPs, and the results were compared with inelastic neutron scattering experiment data. The MLP based on SCAN agreed well with the experimental results. Remarkably, the MLP successfully reproduced the spectral shape in the low-wavenumber, where evaluation requires a long-time simulation with high accuracy.