A Unified Predictive and Generative Solution for Liquid Electrolyte Formulation
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915542997139456 |
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| author | Yang, Zhenze Wu, Yifan Han, Xu Zhang, Ziqing Lai, Haoen Mu, Zhenliang Zheng, Tianze Liu, Siyuan Pu, Zhichen Wang, Zhi Yu, Zhiao Gong, Sheng Yan, Wen |
| author_facet | Yang, Zhenze Wu, Yifan Han, Xu Zhang, Ziqing Lai, Haoen Mu, Zhenliang Zheng, Tianze Liu, Siyuan Pu, Zhichen Wang, Zhi Yu, Zhiao Gong, Sheng Yan, Wen |
| contents | Liquid electrolytes are critical components of next-generation energy storage systems, enabling fast ion transport, minimizing interfacial resistance, and ensuring electrochemical stability for long-term battery performance. However, measuring electrolyte properties and designing formulations remain experimentally and computationally expensive. In this work, we present a unified framework for designing liquid electrolyte formulation, integrating a forward predictive model with an inverse generative approach. Leveraging both computational and experimental data collected from literature and extensive molecular simulations, we train a predictive model capable of accurately estimating electrolyte properties from ionic conductivity to solvation structure. Our physics-informed architecture preserves permutation invariance and incorporates empirical dependencies on temperature and salt concentration, making it broadly applicable to property prediction tasks across molecular mixtures. Furthermore, we introduce -- to the best of our knowledge -- the first generative machine learning framework for molecular mixture design, demonstrated on electrolyte systems. This framework supports multi-condition-constrained generation, addressing the inherently multi-objective nature of materials design. As a proof of concept, we experimentally identified three liquid electrolytes with both high ionic conductivity and anion-concentrated solvation structure. This unified framework advances data-driven electrolyte design and can be readily extended to other complex chemical systems beyond electrolytes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18728 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Unified Predictive and Generative Solution for Liquid Electrolyte Formulation Yang, Zhenze Wu, Yifan Han, Xu Zhang, Ziqing Lai, Haoen Mu, Zhenliang Zheng, Tianze Liu, Siyuan Pu, Zhichen Wang, Zhi Yu, Zhiao Gong, Sheng Yan, Wen Materials Science Liquid electrolytes are critical components of next-generation energy storage systems, enabling fast ion transport, minimizing interfacial resistance, and ensuring electrochemical stability for long-term battery performance. However, measuring electrolyte properties and designing formulations remain experimentally and computationally expensive. In this work, we present a unified framework for designing liquid electrolyte formulation, integrating a forward predictive model with an inverse generative approach. Leveraging both computational and experimental data collected from literature and extensive molecular simulations, we train a predictive model capable of accurately estimating electrolyte properties from ionic conductivity to solvation structure. Our physics-informed architecture preserves permutation invariance and incorporates empirical dependencies on temperature and salt concentration, making it broadly applicable to property prediction tasks across molecular mixtures. Furthermore, we introduce -- to the best of our knowledge -- the first generative machine learning framework for molecular mixture design, demonstrated on electrolyte systems. This framework supports multi-condition-constrained generation, addressing the inherently multi-objective nature of materials design. As a proof of concept, we experimentally identified three liquid electrolytes with both high ionic conductivity and anion-concentrated solvation structure. This unified framework advances data-driven electrolyte design and can be readily extended to other complex chemical systems beyond electrolytes. |
| title | A Unified Predictive and Generative Solution for Liquid Electrolyte Formulation |
| topic | Materials Science |
| url | https://arxiv.org/abs/2504.18728 |