Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design

This is the repository for the Uni-ELF framework.

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. Through a two-stage pretraining, Uni-ELF is able to capture intricate molecular and mixture-level information, and 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.