2, Google Brain, Mountain View, California, United States
We compile data and physics-based machine learned models for solid Li-ion electrolyte performance to assess the state of materials discovery efforts in solid-state batteries and build new insights for future efforts. Candidate electrolyte materials must satisfy several requirements, chief among them fast ionic conductivity and robust electrochemical stability. In order to probe the interplay of these properties, we first build and validate a machine learning-based model for predicting ionic conductivity. We find this model performs 3x better than trial-and-error guessing and successfully identifies several new materials that demonstrate exceptional ionic conductivity. Then, drawing on DFT-based electrochemical stability models, we examine the predicted performance of thousands of candidate materials and quantify the likelihood of breakthrough solid electrolyte discoveries. This analysis suggests that two electrolytes are likely to be necessary in solid-state Li-ion batteries with Li metal anodes. We also find evidence to suggest the halide-based materials may be particularly promising solid electrolyte materials. Given the long time required for new materials characterization and the urgent need for improved energy storage technology, this work is an effort to extract as much information as possible from today’s limited existing data in order to provide a clear path forward for accelerating tomorrow’s efforts.