Electrostatic Potential as Solvent Descriptor to Enable Rational Electrolyte Design for Lithium Batteries

Abstract Artificial intelligence/machine learning (AI/ML) applied to battery research is considered to be a powerful tool for accelerating the research cycle. However, the development of appropriate materials descriptors is often the first hurdle toward implementing meaningful and accurate AI/ML. Currently, rational solvent selection remains a significant challenge in electrolyte development and is still based on experiments. The dielectric constant (ε) and donor number (DN) in electrolyte design are insufficient. Finding theoretically computable solvent descriptors for evaluating Li + solvation is a significant step toward accelerating electrolyte development. Here, based on the electrostatic interaction…

• NN
  National Natural Science Foundation of China

  Awards: U21A20170, 52073161, 22279070
• PR
  Postdoctoral Research Foundation of China

  Award: 2022M720080