Equivariant machine learning of Electric Field Gradients -- Predicting the quadrupolar coupling constant in the MAPbI$_3$ phase transition

TL;DR

This paper introduces a machine learning approach that combines force fields and symmetry-preserving models to accurately predict electric field gradients and quadrupolar coupling constants in disordered materials, validated on MAPbI3 phase transition.

Contribution

It presents a novel integrated machine learning framework that accurately predicts nuclear quadrupolar coupling constants in complex materials at finite temperatures.

Findings

Successfully predicted the phase transition temperature of MAPbI3.

Achieved close agreement with experimental data.

Demonstrated the effectiveness of symmetry-preserving models in disordered systems.

Abstract

We present a strategy combining machine learning and first-principles calculations to achieve highly accurate nuclear quadrupolar coupling constant predictions. Our approach employs two distinct machine-learning frameworks: a machine-learned force field to generate molecular dynamics trajectories and a second model for electric field gradients that preserves rotational and translational symmetries. By incorporating thermostat-driven molecular dynamics sampling, we enable the prediction of quadrupolar coupling constants in highly disordered materials at finite temperatures. We validate our method by predicting the tetragonal-to-cubic phase transition temperature of the organic-inorganic halide perovskite MAPbI$_3$, obtaining results that closely match experimental data.