Development of efficient and high-performing electrolytes is crucial for advancing energy storage technologies, particularly in batteries. Predicting the performance of battery electrolytes rely on complex interactions between the individual constituents. Consequently, a strategy that adeptly captures these relationships and forms a robust representation of the formulation is essential for integrating with machine learning models to predict properties accurately. In this paper, we introduce a novel approach leveraging a transformer-based molecular representation model to effectively and efficiently capture the representation of electrolyte formulations. The performance of the proposed approach is evaluated on two battery property prediction tasks and the results show superior performance compared to the state-of-the-art methods.

翻译：高效高性能电解质的开发对于推进储能技术（尤其是在电池领域）的发展至关重要。电池电解质性能的预测依赖于各组分之间复杂的相互作用。因此，一种能够有效捕捉这些关系并形成配方稳健表征的策略，对于与机器学习模型集成以准确预测性能至关重要。本文提出了一种新方法，利用基于Transformer的分子表征模型来高效且有效地捕捉电解质配方的表征。所提方法的性能在两个电池性能预测任务上进行了评估，结果表明其性能优于现有最先进方法。