Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914851020865536 |
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| author | Priyadarsini, Indra Sharma, Vidushi Takeda, Seiji Kishimoto, Akihiro Hamada, Lisa Shinohara, Hajime |
| author_facet | Priyadarsini, Indra Sharma, Vidushi Takeda, Seiji Kishimoto, Akihiro Hamada, Lisa Shinohara, Hajime |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19792 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model Priyadarsini, Indra Sharma, Vidushi Takeda, Seiji Kishimoto, Akihiro Hamada, Lisa Shinohara, Hajime Machine Learning Emerging Technologies 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. |
| title | Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model |
| topic | Machine Learning Emerging Technologies |
| url | https://arxiv.org/abs/2406.19792 |