金属有机骨架
计算机科学
领域(数学)
财产(哲学)
纳米技术
吞吐量
分离(统计)
生化工程
数据科学
材料科学
机器学习
化学
工程类
电信
哲学
吸附
有机化学
认识论
纯数学
无线
数学
作者
Çiğdem Altıntaş,Omer Faruk Altundal,Seda Keskın,Ramazan Yıldırım
标识
DOI:10.1021/acs.jcim.1c00191
摘要
The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure–performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.
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