Attention-based solubility prediction of polysulfide and electrolyte analysis for lithium-sulfur batteries.

ABSTRACT: During the continuous charge and discharge process in lithium-sulfur batteries, one of the next-generation batteries, polysulfides are generated in the battery's electrolyte, and impact its performance in terms of power and capacity by involving the process. The amount of polysulfides in the electrolyte could be estimated by the change of the Gibbs free energy of the electrolyte, [Formula: see text] in the presence of polysulfide. However, obtaining [Formula: see text] of the diverse mixtures of components in the electrolyte is a complex and expensive task that shows itself as a bottleneck in optimization of electrolytes. In this work, we present a machine-learning approach for predicting [Formula: see text] of electrolytes. The proposed architecture utilizes (1) an attention-based model (

SUBMITTER: Lee J 

PROVIDER: S-EPMC10682475 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature