Artificial Intelligence to Power the Future of Materials Science and Engineering

被引：127
作者：
Sha, Wuxin [1 ,2 ]
Guo, Yaqing [2 ]
Yuan, Qing [1 ]
Tang, Shun [2 ]
Zhang, Xinfang [1 ]
Lu, Songfeng [1 ]
Guo, Xin [3 ]
Cao, Yuan-Cheng [2 ]
Cheng, Shijie [2 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Mat Sci & Engn, Wuhan 430074, Peoples R China
来源：
ADVANCED INTELLIGENT SYSTEMS | 2020年 / 2卷 / 04期
关键词：
artificial intelligence; chemical syntheses; machine learning; materials science; properties predictions; DEEP NEURAL-NETWORKS; MATERIALS DISCOVERY; MACHINE; OPTIMIZATION; DESIGN; GO; POTENTIALS; PREDICTION; CATALYST; GAME;
D O I：
10.1002/aisy.201900143
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial-and-error method is inefficient and time-consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard-coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects.
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页数：12
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