Active learning for effective Hamiltonian of super-large-scale atomic structures

arXiv:2307.08929v2 Announce Type: replace-cross
Abstract: The first-principles-based effective Hamiltonian is widely used to predict and simulate the properties of ferroelectrics and relaxor ferroelectrics. However, the parametrization method of the effective Hamiltonian is complicated and hardly can resolve the systems with complex interactions and/or complex components. Here, we developed an on-the-fly active machine learning approach to parametrize the effective Hamiltonian based on Bayesian linear regression. The parametrization is completed in molecular dynamics simulations, with the energy, forces and stress predicted at …

abstract active learning arxiv components cond-mat.mtrl-sci cs.lg fly however interactions physics.app-ph physics.comp-ph scale systems type