Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model

• URL: http://arxiv.org/abs/2406.19792v1
• Date: Fri, 28 Jun 2024 09:55:29 GMT
• Title: Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model
• Authors: Indra Priyadarsini, Vidushi Sharma, Seiji Takeda, Akihiro Kishimoto, Lisa Hamada, Hajime Shinohara,
• Abstract summary: 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.
• Score: 4.301136099065666
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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