Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference.

By Ryosuke Jinnouchi, J. Lahnsteiner, Ferenc Karsai, Georg Kresse, M. Bokdam. Physical Review Letters, 2019

335 citations recorded by the source corpus.

Realistic finite temperature simulations of matter are a formidable challenge for first principles methods. Long simulation times and large length scales are required, demanding years of computing time. Here we present an on-the-fly machine learning scheme that generates force fields automatically during molecular dynamics simulations. This opens up the required time and length scales, while retaining the distinctive chemical precision of first principles methods and minimizing the need for human intervention. The method is widely applicable to multielement complex systems. We demonstrate its predictive power on the entropy driven phase transitions of hybrid perovskites, which have never been accurately described in simulations. Using machine learned potentials, isothermal-isobaric simulations give direct insight into the underlying microscopic mechanisms. Finally, we relate the phase transition temperatures of different perovskites to the radii of the involved species, and we determine the order of the transitions in Landau theory.

Subfields: Computational Theory and Mathematics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/77ea37089b392cc79da7aab5989b41ed78e9d931

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In Computational Theory and Mathematics

In Materials Chemistry

Citations in the corpus

Methods for comparing uncertainty quantifications for material property predictions (2019, methodology): “[12] have used online active learning to accelerate molecular dynamics simulations.”