Free energy of (CoxMn1-x)3O4 mixed phases from machine-learning-enhanced ab initio calculations

TL;DR

This study compares machine learning methods to predict the free energy and phase diagrams of (CoxMn1-x)3O4, aiding high-temperature energy storage material design by improving computational efficiency and accuracy.

Contribution

It introduces and evaluates three ML approaches for sampling configuration space in complex mixed phases, addressing challenges in ab initio free energy calculations.

Findings

ML models require data pre-treatment for accuracy

Energy predictions are harder at compositions with competing ground states

ML approaches can effectively screen materials for thermochemical energy storage

Abstract

(CoxMn1-x)3O4 is a promising candidate material for solar thermochemical energy storage. A high-temperature model for this system would provide a valuable tool for evaluating its potential. However, predicting phase diagrams of complex systems with ab initio calculations is challenging due to the varied sources affecting the free energy, and with the prohibitive amount of configurations needed in the configurational entropy calculation. In this work, we compare three different machine learning (ML) approaches for sampling the configuration space of (CoxMn1-x)3O4, including a simpler ML approach, which would be suitable for application in high-throughput studies. We use experimental data for a feature of the phase diagram to assess the accuracy of model predictions. We find that with some methods, data pre-treatment is needed to obtain accurate predictions due to inherently composition-imbalanced training data for a mixed phase. We highlight that the important entropy contributions depend on the physical regimes of the system under investigation and that energy predictions with ML models are more challenging at compositions where there are energetically competing ground state crystal structures. Similar methods to those outlined here can be used to screen other candidate materials for thermochemical energy storage