Vision for energy material design:A roadmap for integrated data-driven modeling

被引：8
|
作者：
Zhilong Wang [1 ]
Yanqiang Han [1 ]
Junfei Cai [1 ]
An Chen [1 ]
Jinjin Li [1 ]
机构：
[1] Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University
来源：
Journal of Energy Chemistry | 2022年 / 71卷 / 08期
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
D O I：
暂无
中图分类号：
TB30 [工程材料一般性问题]; TP181 [自动推理、机器学习];
学科分类号：
摘要：
The application scope and future development directions of machine learning models(supervised learning, transfer learning, and unsupervised learning) that have driven energy material design are discussed.
引用
收藏
页码：56 / 62 +7
页数：8
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