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Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materials
Optimizing synthesis parameters is the key to successfully design ideal Ni-rich cathode materials that satisfy principal electrochemical specifications. We herein implement machine learning algorithms using 330 experimental datasets, obtained from a controlled environment for reliability, to constru...
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| Pubblicato in: | Sci Rep |
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| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Nature Publishing Group UK
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6202356/ https://ncbi.nlm.nih.gov/pubmed/30361533 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-018-34201-4 |
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