Fig. 1: Background and design of experiments (DoE) formulation.
From: Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries
a Schematic illustration of Cu | |LFP cell configuration as an example of anode-free lithium metal batteries (LMBs) utilized in the present study. b Illustrative examples for anode-free LMB capacity retention curves. The curves shown do not represent real data. Discharge capacity at 20th cycle normalized with respect to positive electrode theoretical capacity is selected as target property to balance the effects of initial Coulombic efficiency and long-term stability. c Workflow describing creation of the virtual search space. d Schematic illustration of the active learning workflow, consisting of five steps in an iterative closed-loop, leading to batched optimization of liquid electrolytes compatible with anode-free LMBs — training of surrogate models, inference on virtual search space (unlabeled), ranking of predicted candidates by the oracle (acquisition function), acquiring purchasable/synthesizable candidates, experimental battery testing, and feedback to the surrogate models. GPs = Gaussian processes, (\({{\boldsymbol{x}}}\),y) = input features and labels (normalized discharge capacity at 20th cycle) for in-house dataset; (\(\widetilde{{{\boldsymbol{x}}}}\), \(\widetilde{\mu }\), \(\widetilde{\sigma }\)) = input features, predicted mean and uncertainty for electrolyte target property in virtual search space, \({{{\boldsymbol{x}}}}^{{{\boldsymbol{*}}}}\) = best observed value, and EI = expected improvement.
