Exploring librational pathways with on-the-fly machine-learning force fields: Methylammonium molecules in MAPbX$_3$ (X=I, Br, Cl) perovskites

TL;DR

This study employs on-the-fly machine-learning force fields to efficiently explore librational pathways and structural stability of methylammonium molecules in MAPbX₃ perovskites, revealing temperature-dependent anharmonic motions.

Contribution

It introduces a machine-learning force field approach to determine thermodynamically stable structures of perovskites, reducing computational costs significantly.

Findings

Identified temperature-dependent librational pathways.

Distinguished structural differences via thermal evolution.

Validated structures against experimental data.

Abstract

Two seemingly similar crystal structures of the low-temperature (~100 K) MAPbX$_3$ (X=I,Br,Cl) perovskites, but with different relative Methylammonium (MA) ordering, have appeared as representatives of this orthorhombic phase. Distinguishing them by X-ray diffraction experiments is difficult and conventional first-principles based molecular-dynamics approaches are often too computationally intensive to be feasible. Therefore, to determine the thermodynamically stable structure, we use a recently introduced on-the-fly Machine-Learning Force Field method, which reduces the computation time from years to days. The molecules exhibit a large degree of anharmonic motion depending on temperature: i.e. rattling, twisting and tumbling. We observe the crystal's 'librational pathways' while slowly heating it in isothermal-isobaric simulations. Marked differences in the thermal evolution of structural parameters allow us to determine the real structure of the system via a comparison with experimentally determined crystal structures.