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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Detalles Bibliográficos
Autores principales: Min, Kyoungmin, Choi, Byungjin, Park, Kwangjin, Cho, Eunseog
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202356/
https://www.ncbi.nlm.nih.gov/pubmed/30361533
http://dx.doi.org/10.1038/s41598-018-34201-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202356/
https://www.ncbi.nlm.nih.gov/pubmed/30361533
http://dx.doi.org/10.1038/s41598-018-34201-4

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