Artificial intelligence and machine learning for targeted energy storage solutions

被引：37
|
作者：
Barrett, Dean H. [1 ]
Haruna, Aderemi [1 ]
机构：
[1] Univ Witwatersrand, Inst Mol Sci, Sch Chem, Private Bag 3,PO Wits, ZA-2050 Johannesburg, South Africa
来源：
CURRENT OPINION IN ELECTROCHEMISTRY | 2020年 / 21卷
关键词：
Artificial Intelligence; Machine Learning; Battery-Energy Storage; Deep Learning; Database; Materials Computation; Algorithm; ELECTRODE MATERIALS; LITHIUM; DESIGN; INTERFACE; DISCOVERY; CATHODE; PROJECT; ANODE;
D O I：
10.1016/j.coelec.2020.02.002
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
With the application of machine learning to large-material data sets, models are being developed that allow us to better predict novel materials with designed properties. Advances in artificial intelligence and its subclasses, as well as compute infrastructure, are making it possible to rapidly compute material properties, to access time/length scales and chemical spaces beyond the current capabilities of density functional theory and to outperform humans in interpretation and characterization of the data. This review highlights the latest developments in the field with special interest to energy storage materials.
引用
收藏
页码：160 / 166
页数：7
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