  (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
