Expanding materials science with universal many-body graph neural networks

被引：2
|
作者：
Pan, Jie
机构：
来源：
NATURE COMPUTATIONAL SCIENCE | 2022年 / 2卷 / 11期
关键词：
D O I：
10.1038/s43588-022-00360-8
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A universal interatomic potential for the periodic table has been developed by combining graph neural networks with three-body interactions. This M3GNet potential can perform structural relaxations, dynamic simulations and property predictions for materials across a diverse chemical space.
引用
收藏
页码：703 / 704
页数：2
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