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

• URL: http://arxiv.org/abs/2407.06152v1
• Date: Mon, 8 Jul 2024 17:26:49 GMT
• Title: Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design
• Authors: Boshen Zeng, Sian Chen, Xinxin Liu, Changhong Chen, Bin Deng, Xiaoxu Wang, Zhifeng Gao, Yuzhi Zhang, Weinan E, Linfeng Zhang,
• Abstract summary: 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. Through this comprehensive pretraining, Uni-ELF is able to capture intricate molecular and mixture-level information, which significantly enhances its predictive capability. We believe this innovative framework will pave the way for automated AI-based electrolyte design and engineering.
• Score: 20.855235423913285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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