Unsupervised discovery of solid-state lithium ion conductors

Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning...

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Detalles Bibliográficos
Autores principales: Zhang, Ying, He, Xingfeng, Chen, Zhiqian, Bai, Qiang, Nolan, Adelaide M., Roberts, Charles A., Banerjee, Debasish, Matsunaga, Tomoya, Mo, Yifei, Ling, Chen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6868160/
https://www.ncbi.nlm.nih.gov/pubmed/31748523
http://dx.doi.org/10.1038/s41467-019-13214-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6868160/
https://www.ncbi.nlm.nih.gov/pubmed/31748523
http://dx.doi.org/10.1038/s41467-019-13214-1

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