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

ABSTRACT: 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 retentio

SUBMITTER: Ma P 

PROVIDER: S-EPMC12462480 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature