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

Author(s)
Ryosuke Jinnouchi, Jonathan Lahnsteiner, Ferenc Karsai, Georg Kresse, Menno Bokdam
Abstract

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.

Organisation(s)
Computational Materials Physics
External organisation(s)
Toyota Central R&D Labs, VASP Software GmbH
Journal
Physical Review Letters
Volume
122
No. of pages
5
ISSN
0031-9007
DOI
https://doi.org/10.1103/PhysRevLett.122.225701
Publication date
06-2019
Peer reviewed
Yes
Austrian Fields of Science 2012
Materials physics
Keywords
Portal url
https://ucris.univie.ac.at/portal/en/publications/phase-transitions-of-hybrid-perovskites-simulated-by-machinelearning-force-fields-trained-on-the-fly-with-bayesian-inference(1ad2c78b-2d4b-4a68-b137-9d1ab4bb6c25).html