Universal machine learning potential accelerates atomistic modeling of materials

被引：1

作者：

Zhongheng Fu ^{[1
,2
]}

Dawei Zhang ^{[1
,2
]}

机构：

[1] Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing

[2] National Materials Corrosion and Protection Data Center, University of Science and Technology Beijing

来源：

Journal of Energy Chemistry | 2023年 / 83卷 / 08期

基金：

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：

0805 ; 080502 ; 081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the rapid development of computer techniques,atomistic modeling is playing an increasingly important role in understanding the structure-activity relationship of materials.Molecular dynamics (MD) is a computational simulation approach to predicting the structural evolution of an atomic system over time,widely used to understand physical and chemical phenomena including phase transition,diffusion,crystallization,and reaction [1].

引用

页码：1 / 2

页数：2

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