Electrolytomics: A unified big data approach for electrolyte design and discovery

Electrolyte discovery is the bottleneck for developing next generation batteries. For example, lithium metal batteries (LMBs) promise to double the energy density of current Li-ion batteries (LIBs) while next generation LIBs are desired for operations at extreme temperature conditions and with high voltage cathodes. However, there are no suitable electrolytes to support these battery chemistries. Electrolyte requirements are complex (conductivity, stability, safety) and the chemical design space (salts, solvents, additives, composition) is practically infinite; hence discovery is primarily guided through trial-and-error which slows the deployment of new battery chemistries. Inspired by artificial intelligence (AI)-enabled drug discovery, we usher in a new paradigm for electrolyte discovery. We assemble the largest small molecule experimental liquid electrolyte ionic conductivity dataset and build highly accurate machine learning (ML) and deep learning models to predict ionic conductivity across a wide range of electrolyte classes. The developed models outperform molecular dynamic (MD) simulations and are interpretable without explicit encoding of ionic solvation. While most ML-based approaches target a single property, we build additional models of oxidative stability and Coulombic efficiency and develop a new metric called the electrolyte score (eScore) to unify the predicted disparate electrolyte properties. Deploying these models on large unlabeled datasets, we discover new electrolyte solvents, experimentally validate that the electrolyte is conductive (> 1 mS cm-1), stable up to 6V, supports efficient anode-free LMB, and even LIB cycling at extreme temperatures. Our work heralds a new age in electrolyte design and battery materials discovery.