Uni-ELF：一种用于电解液配方设计的多层次表示学习框架
Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design
AI总结：
针对电解液设计缺乏计算-实验闭环、性能预测不准的问题，提出两阶段预训练的多层次表示学习框架Uni-ELF，在分子与配方性质预测上优于现有方法，可集成到自动化实验工作流，助力AI驱动的电解液设计。
摘要：
锂电池技术的进步高度依赖电解液的设计与工程优化。然而，当前的电解液分子设计与配方优化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液配方性能。本研究提出了Uni-ELF，这是一种新型多层次表示学习框架，旨在推动电解液设计发展。该方法采用两阶段预训练：在分子层面利用Uni-Mol模型重构三维分子结构，在混合物层面预测分子动力学模拟得到的统计结构性质（如径向分布函数）。通过这种全面的预训练，Uni-ELF能够捕捉复杂的分子与混合物层面信息，显著提升其预测能力。实验结果表明，Uni-ELF在预测分子性质（如熔点、沸点、可合成性）和配方性质（如电导率、库仑效率）两方面均大幅优于现有最优方法。此外，Uni-ELF可无缝集成到自动化实验设计工作流中。我们认为这一创新框架将为基于AI的自动化电解液设计与工程开辟道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
