Active-Learning for Machine-Learned Interatomic Potentials; The Example of Strongly Anharmonic Materials

Machine-learned interatomic potentials (MLIP) promise to perform efficient molecular dynamics simulations with the accuracy of ab initio methods for large supercells and long time spans, which are not feasible with ab initio methods. For strongly anharmonic materials, it is crucial to capture rare anharmonic effects, such as the formation of intrinsic defects and dynamical precursors to phase transitions [1]. To expedite the training process for such rare events, we developed an active-learning scheme that utilizes molecular dynamics with MLIP (MLIP-MD) and incorporates a measure of uncertainty to identify qualitative deviations from the model's trained region. Our applications to KCaF3 and CuI demonstrate a favorable training speed. Our MLIP-MD runs adeptly and captures essential dynamical features, including anharmonicity measure for molecular dynamics trajectory, and anharmonic vibrations, as well as rare anharmonic events. We also discuss how to employ this approach for predicting the electrical conductivity of strongly anharmonic materials using the ab initio Kubo-Greenwood approach.

[1] F. Knoop et al. Phys. Rev. Lett. 130, 236301 (2023).