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MM: Fachverband Metall- und Materialphysik
MM 18: SYMD contributed
MM 18.3: Vortrag
Mittwoch, 19. März 2025, 10:45–11:00, H23
Active learning-based automated construction of Hamiltonian for structural phase transitions: a case study on BaTiO3 — •Mian Dai1, Yixuan Zhang1, Nuno Fortunato1, Peng Chen2, and Hongbin Zhang1 — 1Institute of Materials Science, Technical University of Darmstadt, Darmstadt 64287, Germany — 2Physics Department and Institute for Nanoscience and Engineering, University of Arkansas, Fayetteville, Arkansas 72701, USA
The effective Hamiltonians have been widely applied to simulate the phase transitions in polarizable materials, with coefficients obtained by fitting to accurate first-principles calculations. However, it is tedious to generate distorted structures with symmetry constraints, in particular when high-ordered terms are considered. In this work, we implement and apply a Bayesian optimization-based approach to sample the potential energy surface, automating the Hamiltonian construction by selecting distorted structures via active learning. Taking BaTiO3 (BTO) as an example, we demonstrate that the Hamiltonian can be obtained using fewer than 30 distorted structures. Follow-up Monte Carlo simulations can reproduce the structural phase transition temperatures of BTO, comparable to experimental values with an error < 10%. Our approach can be straightforwardly applied on other polarizable materials and paves the way for quantitative atomistic modelling of diffusionless phase transitions.
Keywords: Density functional theory; structural phase transition; Bayesian optimization; Active learning