Theoretical and computational methods for accelerated materials discovery

被引：6

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

页数：17

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