# Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference

## TL;DR

This paper introduces an on-the-fly machine learning approach to generate force fields for molecular dynamics, enabling accurate, large-scale simulations of hybrid perovskites' phase transitions with minimal human intervention.

## Contribution

The authors develop a versatile, automated machine learning scheme for force field generation during simulations, specifically applied to complex hybrid perovskites to accurately model phase transitions.

## Key findings

- Successfully simulated entropy-driven phase transitions in hybrid perovskites.
- Linked phase transition temperatures to ionic radii of involved species.
- Determined the order of phase transitions using Landau theory.

## 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 compute 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 multi-element 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.