  Recent advances in machine learning (ML) have expedited materials discovery
and design. One significant challenge faced in ML for materials is the
expansive combinatorial space of potential materials formed by diverse
constituents and their flexible configurations. This complexity is particularly
evident in molecular mixtures, a frequently explored space for materials such
as battery electrolytes. Owing to the complex structures of molecules and the
sequence-independent nature of mixtures, conventional ML methods have
difficulties in modeling such systems. Here we present MolSets, a specialized
ML model for molecular mixtures. Representing individual molecules as graphs
and their mixture as a set, MolSets leverages a graph neural network and the
deep sets architecture to extract information at the molecule level and
aggregate it at the mixture level, thus addressing local complexity while
retaining global flexibility. We demonstrate the efficacy of MolSets in
predicting the conductivity of lithium battery electrolytes and highlight its
benefits in virtual screening of the combinatorial chemical space.
