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.

高效高性能电解液的开发对于推进能量存储技术，特别是电池技术非常重要。本文介绍了一种新的方法，利用基于 Transformer 的分子表示模型来有效地捕捉电解液配方的表示，通过在两个电池属性预测任务中对该方法进行评估，结果表明其在性能上优于目前的最先进方法。