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Electrolyte solutions play critical role in a vast range of importantapplications, yet an accurate and scalable method of predicting theirproperties without fitting to experiment has remained out of reach, despiteover a century of effort. Here, we combine state-of-the-art density functionaltheory and equivariant neural network potentials to demonstrate thiscapability, reproducing key structural, thermodynamic, and kinetic properties.We show that neural network potentials (NNPs) can be recursively trained on asubset of their own output to enable coarse-grained/continuum-solvent molecularsimulations that can access much longer timescales than possible with all atomsimulations. We observe the surprising formation of Li cation dimers along withidentical anion-anion pairing of chloride and bromide anions. Finally, wereproduce simulate the crystal phase and infinite dilution pairing freeenergies despite being trained only on moderate concentration solutions. Thisapproach should be scaled to build a greatly expanded database of electrolytesolution properties than currently exists.

Scalable molecular simulation of electrolyte solutions with quantum chemical accuracy