期刊
COORDINATION CHEMISTRY REVIEWS
卷 484, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.ccr.2023.215112
关键词
Metal organic framework; Molecular simulation; Computational screening; Big data; Machine learning; Data science
The reticular chemistry of MOFs enables the generation of countless materials, some of which can replace traditional porous materials in various fields. High-throughput computational screening approaches based on molecular simulations are used to identify optimal MOFs. However, more efficient methods are still needed due to the rapidly growing number of MOFs.
The reticular chemistry of metal-organic frameworks (MOFs) allows for the generation of an almost boundless number of materials some of which can be a substitute for the traditionally used porous mate-rials in various fields including gas storage and separation, catalysis, drug storage and delivery. The num-ber of MOFs and their potential applications are growing so quickly that, when novel MOFs are synthesized, testing them for all possible applications is not practical. High-throughput computational screening approaches based on molecular simulations of materials have been widely used to investigate MOFs and identify the optimal MOFs for a specific application. Despite the growing computational resources, given the enormous MOF material space, computational identification of promising MOFs requires more efficient approaches in terms of time and effort. Leveraging data-driven science techniques can offer key benefits such as accelerated MOF design and discovery pathways via the establishment of machine learning (ML) models and interpretation of complex structure-performance relationships that can reach beyond expert intuition. In this review, we present key scientific breakthroughs that propelled computational modeling of MOFs and discuss the state-of-the-art approaches extending from molecular simulations to ML algorithms. Finally, we provide our perspective on the potential opportunities and challenges for the future of big data-driven MOF design and discovery. (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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