Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte

Traditionally, the discovery of new materials has often depended on scholars’ computational and experimental experience. The traditional trial-and-error methods require many resources and computing time. Due to new materials’ properties becoming more complex, it is difficult to predict and identify...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Qianyu, Chen, Kunfeng, Liu, Fei, Zhao, Mengying, Liang, Feng, Xue, Dongfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840428/
https://www.ncbi.nlm.nih.gov/pubmed/35161101
http://dx.doi.org/10.3390/ma15031157

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840428/
https://www.ncbi.nlm.nih.gov/pubmed/35161101
http://dx.doi.org/10.3390/ma15031157

Ejemplares similares