Vibrational properties of metastable polymorph structures by machine learning

TL;DR

This paper demonstrates that machine learning can efficiently estimate vibrational properties of metastable structures from atomic positions, significantly reducing computational costs compared to traditional ab initio methods.

Contribution

The study introduces a machine learning approach using random forests to predict vibrational properties from atomic positions, enabling rapid stability assessments.

Findings

Achieved low error (0.17 eV/Ų) in force constant prediction.

Predicted vibrational properties closely match ab initio results.

Method significantly reduces computational expense for stability analysis.

Abstract

Despite vibrational properties being critical for the ab initio prediction of the finite temperature stability and transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on only 121 metastable structures of KZnF$_3$ reaches a maximum absolute error of 0.17 eV/$\textrm\AA^2$ for the interatomic force constants, and it is much less expensive than training the complete force field for such compound. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures.