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Machine learning accelerates identification of lithiated phases in X-ray images of battery hosts
Santos et al. (2022) propose a machine learning-based approach to identify various lithiated phases across lengthscales in X-ray images of battery particles, thus enabling automatic interpretation of such information in much bigger datasets and creating opportunities to unravel previously inaccessib...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768674/ https://www.ncbi.nlm.nih.gov/pubmed/36569544 http://dx.doi.org/10.1016/j.patter.2022.100654 |
| Sumario: | Santos et al. (2022) propose a machine learning-based approach to identify various lithiated phases across lengthscales in X-ray images of battery particles, thus enabling automatic interpretation of such information in much bigger datasets and creating opportunities to unravel previously inaccessible scientific understanding. |
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