  Advancements in lithium battery technology heavily rely on the design and
engineering of electrolytes. However, current schemes for molecular design and
recipe optimization of electrolytes lack an effective
computational-experimental closed loop and often fall short in accurately
predicting diverse electrolyte formulation properties. In this work, we
introduce Uni-ELF, a novel multi-level representation learning framework to
advance electrolyte design. Our approach involves two-stage pretraining:
reconstructing three-dimensional molecular structures at the molecular level
using the Uni-Mol model, and predicting statistical structural properties
(e.g., radial distribution functions) from molecular dynamics simulations at
the mixture level. Through this comprehensive pretraining, Uni-ELF is able to
capture intricate molecular and mixture-level information, which significantly
enhances its predictive capability. As a result, Uni-ELF substantially
outperforms state-of-the-art methods in predicting both molecular properties
(e.g., melting point, boiling point, synthesizability) and formulation
properties (e.g., conductivity, Coulombic efficiency). Moreover, Uni-ELF can be
seamlessly integrated into an automatic experimental design workflow. We
believe this innovative framework will pave the way for automated AI-based
electrolyte design and engineering.
