Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries

Anode-free or 'zero-excess' lithium metal batteries offer high energy density compared to current lithium-ion batteries but require electrolyte innovation to extend cycle life. Due to the lack of universal design principles, electrolyte development for anode-free lithium metal batteries is slow and incremental and mainly driven by trial-and-error. Here, we demonstrate the use of active learning as an alternative approach to accelerate electrolyte discovery for anode-free lithium metal batteries. Unlike conventional data-intensive frequentist machine learning techniques, our active learning framework employs sequential Bayesian experimental design with Bayesian model averaging to efficiently identify optimal candidates in typical data-scarce and noisy label settings. Using capacity retention in real Cu||LiFePO4 cells as the target property, our approach integrates experimental feedback to iteratively refine predictions. Starting with just 58 data points from an in-house cycling dataset, the active learning framework explored a virtual search space of 1 million electrolytes, rapidly converging on optimal candidates. After seven active learning campaigns with about ten electrolytes tested in each, four distinct electrolyte solvents are identified that rival state-of-the-art electrolytes in performance. This work showcases the promise of active learning approaches in navigating large electrolyte chemical spaces for next-generation batteries.