Besides other applications of such surrogate models for solid solutions, further work should focus on developing more efficient training strategies to reduce the number of configurations required. This could be inspired by the efficient methods for selecting training data for cluster expansions , and should be of high relevance for more complex compositions. Furthermore, improvements may be observed with other types of machine learning architecture such as equivariant neural network potentials . For the ones with vibrational entropy included, the red curve marked by an asterisk (*) is the one where the vibrational free energies of the end-members are calculated with DFT. Experimental results from Ref. are given for comparison.