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.

翻译：锂电池技术的进步在很大程度上依赖于电解质的设计与工程。然而，当前电解质分子设计与配方优化的方案缺乏有效的计算-实验闭环，且往往难以准确预测多样化的电解质配方性质。本文中，我们提出了Uni-ELF，一种新颖的多层次表示学习框架，以推进电解质设计。我们的方法包含两阶段预训练：在分子层面利用Uni-Mol模型重建三维分子结构，以及在混合物层面根据分子动力学模拟预测统计结构性质（例如径向分布函数）。通过这种全面的预训练，Uni-ELF能够捕获复杂的分子层面和混合物层面的信息，从而显著提升其预测能力。因此，Uni-ELF在预测分子性质（例如熔点、沸点、可合成性）和配方性质（例如电导率、库仑效率）方面均大幅优于现有最先进方法。此外，Uni-ELF可以无缝集成到自动化实验设计工作流中。我们相信这一创新框架将为基于人工智能的自动化电解质设计与工程开辟道路。