A comprehensive data network for data-driven study of battery materials
Data-driven material research for property prediction and material design using machine learning methods requires a large quantity, wide variety, and high-quality materials data. For battery materials, which are commonly polycrystalline, ceramics, and composites, multiscale data on substances, mater...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Science and Technology of Advanced Materials |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/14686996.2024.2403328 |
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