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 from molecular dynamics simulations at the mixture level. Through this comprehensive pretraining%2C Uni-ELF is able to capture intricate molecular and mixture-level information%2C which significantly enhances its predictive capability. As a result%2C Uni-ELF substantially outperforms state-of-the-art methods in predicting both molecular properties (e.g.%2C melting point%2C boiling point%2C synthesizability) and formulation properties (e.g.%2C conductivity%2C Coulombic efficiency). Moreover%2C 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.?quality=80&w=800)
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