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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.11032 |
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| _version_ | 1866912421349687296 |
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| author | Chen, Wenjie Lin, Zichang Zhang, Xinxin Zhou, Hao Zhang, Yuegang |
| author_facet | Chen, Wenjie Lin, Zichang Zhang, Xinxin Zhou, Hao Zhang, Yuegang |
| contents | Magnesium-ion batteries hold promise as future energy storage solution, yet current Mg cathodes are challenged by low voltage and specific capacity. Herein, we present an AI-driven workflow for discovering high-performance Mg cathode materials. Utilizing the common characteristics of various ionic intercalation-type electrodes, we design and train a Crystal Graph Convolutional Neural Network model that can accurately predicts electrode voltages for various ions with mean absolute errors (MAE) between 0.25 and 0.33 V. By deploying the trained model to stable Mg compounds from Materials Project and GNoME AI dataset, we identify 160 high voltage structures out of 15,308 candidates with voltages above 3.0 V and volumetric capacity over 800 Ah/L. We further train a precise NequIP model to facilitate accurate and rapid simulations of Mg ionic conductivity. From the 160 high voltage structures, the machine learning molecular dynamics simulations have selected 23 cathode materials with both high energy density and high ionic conductivity. This AI-driven workflow dramatically boosts the efficiency and precision of material discovery for multivalent ion batteries, paving the way for advanced Mg battery development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Driven Accelerated Discovery of Intercalation-type Cathode Materials for Magnesium Batteries Chen, Wenjie Lin, Zichang Zhang, Xinxin Zhou, Hao Zhang, Yuegang Materials Science Magnesium-ion batteries hold promise as future energy storage solution, yet current Mg cathodes are challenged by low voltage and specific capacity. Herein, we present an AI-driven workflow for discovering high-performance Mg cathode materials. Utilizing the common characteristics of various ionic intercalation-type electrodes, we design and train a Crystal Graph Convolutional Neural Network model that can accurately predicts electrode voltages for various ions with mean absolute errors (MAE) between 0.25 and 0.33 V. By deploying the trained model to stable Mg compounds from Materials Project and GNoME AI dataset, we identify 160 high voltage structures out of 15,308 candidates with voltages above 3.0 V and volumetric capacity over 800 Ah/L. We further train a precise NequIP model to facilitate accurate and rapid simulations of Mg ionic conductivity. From the 160 high voltage structures, the machine learning molecular dynamics simulations have selected 23 cathode materials with both high energy density and high ionic conductivity. This AI-driven workflow dramatically boosts the efficiency and precision of material discovery for multivalent ion batteries, paving the way for advanced Mg battery development. |
| title | AI-Driven Accelerated Discovery of Intercalation-type Cathode Materials for Magnesium Batteries |
| topic | Materials Science |
| url | https://arxiv.org/abs/2412.11032 |