Machine learning glasses

被引：6
|
作者：
Biroli, Giulio [1 ]
机构：
[1] Univ Paris, Sorbonne Univ, Univ PSL, Lab Phys,ENS,CNRS, Paris, France
来源：
NATURE PHYSICS | 2020年 / 16卷 / 04期
关键词：
4;
D O I：
10.1038/s41567-020-0873-1
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
Artificial neural networks now allow the dynamics of supercooled liquids to be predicted from their structure alone in an unprecedented way, thus providing a powerful new tool to study the physics of the glass transition.
引用
收藏
页码：373 / 374
页数：2
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