Preview of machine learning the quantum-chemical properties of metal-organic frameworks for accelerated materials discovery

被引：6
|
作者：
Callaghan, Sarah [1 ]
机构：
[1] Cell Press, 50 Hampshire St, Cambridge, MA USA
来源：
PATTERNS | 2021年 / 2卷 / 04期
关键词：
D O I：
10.1016/j.patter.2021.100239
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Metal-organic frameworks (MOFs) are a class of chemical compounds used for the storage of gases such as hydrogen and carbon dioxide. They also have potential applications in gas purification, catalysis and as supercapacitors. A database of quantum-chemical properties for over 14,000 MOF structures (the ``QMOF database'') has been created and made available to the community along with code for machine learning and other related resources.
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页数：2
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