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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2412.00498 |
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| _version_ | 1866909410486386688 |
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| author | Chen, Xiang Liu, Mingkang Yin, Shiqiu Gao, Yu-Chen Yao, Nan Zhang, Qiang |
| author_facet | Chen, Xiang Liu, Mingkang Yin, Shiqiu Gao, Yu-Chen Yao, Nan Zhang, Qiang |
| contents | Electrolyte is a very important part of rechargeable batteries such as lithium batteries. However, the electrolyte innovation is facing grand challenges due to the complicated solution chemistry and infinite molecular space (>1060 for small molecules). This work reported an artificial intelligence (AI) platform, namely Uni-Electrolyte, for designing advanced electrolyte molecules, which mainly includes three parts, i.e. EMolCurator, EMolForger, and EMolNetKnittor. New molecules can be designed by combining high-throughput screening and generative AI models from more than 100 million alternative molecules in the EMolCurator module. The molecule properties including frontier molecular orbital information, formation energy, binding energy with a Li ion, viscosity, and dielectric constant, can be adopted as the screening parameters. The EMolForger, and EMolNetKnittor module can predict the retrosynthesis pathway and reaction pathway with electrodes for a given molecule, respectively. With the assist of advanced AI methods, the Uni-Electrolyte is strongly supposed to discover new electrolyte molecules and chemical principles, promoting the practical application of next-generation rechargeable batteries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00498 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uni-Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries Chen, Xiang Liu, Mingkang Yin, Shiqiu Gao, Yu-Chen Yao, Nan Zhang, Qiang Materials Science Electrolyte is a very important part of rechargeable batteries such as lithium batteries. However, the electrolyte innovation is facing grand challenges due to the complicated solution chemistry and infinite molecular space (>1060 for small molecules). This work reported an artificial intelligence (AI) platform, namely Uni-Electrolyte, for designing advanced electrolyte molecules, which mainly includes three parts, i.e. EMolCurator, EMolForger, and EMolNetKnittor. New molecules can be designed by combining high-throughput screening and generative AI models from more than 100 million alternative molecules in the EMolCurator module. The molecule properties including frontier molecular orbital information, formation energy, binding energy with a Li ion, viscosity, and dielectric constant, can be adopted as the screening parameters. The EMolForger, and EMolNetKnittor module can predict the retrosynthesis pathway and reaction pathway with electrodes for a given molecule, respectively. With the assist of advanced AI methods, the Uni-Electrolyte is strongly supposed to discover new electrolyte molecules and chemical principles, promoting the practical application of next-generation rechargeable batteries. |
| title | Uni-Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries |
| topic | Materials Science |
| url | https://arxiv.org/abs/2412.00498 |