  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.
