Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation.
Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation.
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DOI：
10.1021/acs.jcim.1c00191
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发表时间：
2021-05-24
影响因子：
5.6
通讯作者：
Yildirim R
中科院分区：
文献类型：
--
作者：
Altintas C;Altundal OF;Keskin S;Yildirim R
新金属有机框架（MOFs）设计的加速使科学家们专注于高通量计算筛选（HTCS）方法，以快速评估这些迷人材料在各种应用中的前景。HTCS研究为MOFs提供了大量的结构特性和性能数据，需要进一步分析。机器学习（ML）是另一个不断发展的研究领域，最近对MOFs的HTCS的实施非常富有成效，不仅揭示了材料隐藏的结构-性能关系，而且还了解了它们在不同应用中的性能趋势，特别是气体储存和分离。在这篇综述中，我们强调了目前最先进的ML辅助计算筛选气体存储和分离的MOF，并强调ML和MOF模拟的合并如何有用，以解决这个新领域中出现的机遇和挑战。
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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影响因子：
46.2
作者：
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通讯作者：
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通讯作者：
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作者：
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通讯作者：
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