Document Type
Article
Publication Date
2-3-2025
Keywords
density functional theory, structural phase transition, Bayesian optimization, active learning
Abstract
The effective Hamiltonians have been widely applied to simulate the phase transitions inpolarizable materials, with coefficients obtained by fitting to accurate first-principlescalculations. 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 aBayesian optimization-based approach to sample potential energy surfaces, automating theeffective Hamiltonian construction by selecting distorted structures via active learning. Taking BaTiO3(BTO) as an example, we demonstrate that the effective Hamiltonian can be obtainedusing fewer than 30 distorted structures. Follow-up Monte Carlo simulations can reproduce thestructural phase transition temperatures of BTO, comparable to experimental values with anerror< 10%. Our approach can be straightforwardly applied on other polarizable materials and paves the way for quantitative atomistic modelling of diffusion less phase transitions.
Citation
Dai, M., Zhang, Y., Fortunato, N., Chen, P., & Zhang, H. (2025). Active learning-based automated construction of Hamiltonian for structural phase transitions: a case study on BaTiO3. JOURNAL OF PHYSICS-CONDENSED MATTER, 37 (5) https://doi.org/10.1088/1361-648X/ad882a
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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