Theoretical and computational methods for accelerated materials discovery

被引:5
|
作者
Zarkevich, Nikolai A. [1 ]
机构
[1] US DOE, Ames Lab, Ames, IA 50011 USA
来源
MODERN PHYSICS LETTERS B | 2021年 / 35卷 / 12期
关键词
Theory; artificial intelligence; machine learning; multi-scaling; materials discovery; INTERATOMIC POTENTIALS; TRANSFORMATION; HYSTERESIS; TRANSITION; ALLOYS; ENERGY; ENTROPY; STATE;
D O I
10.1142/S0217984921300039
中图分类号
O59 [应用物理学];
学科分类号
摘要
Predicting properties of materials and phase transformation using theoretical and computational multi-scale methods involving artificial intelligence and machine learning (ML) is important and highly rewarding. We review the relevant methods and mention a few applications and examples. We demonstrate that theoretical guidance narrows the search space and accelerates materials discovery.
引用
收藏
页数:17
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