Lee Sangwon, Kim Baekjun, Cho Hyun, Lee Hooseung, Lee Sarah Yunmi, Cho Eun Seon, Kim Jihan
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
Department of Chemistry, Yonsei University, Seoul 03722, Republic of Korea.
ACS Appl Mater Interfaces. 2021 May 26;13(20):23647-23654. doi: 10.1021/acsami.1c02471. Epub 2021 May 14.
In the past decade, there has been an increasing number of computational screening works to facilitate finding optimal materials for a variety of different applications. Unfortunately, most of these screening studies are limited to their initial set of materials and result in a brute-force type of screening approach. In this work, we present a systematic strategy that can find metal-organic frameworks (MOFs) with the desired properties from an extremely diverse and large set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm cm and 96 MOFs with methane working capacity over the current world record of 208 cm cm. We believe that this methodology can take advantage of the modular nature of MOFs and can readily be extended to other important applications as well.
在过去十年中，为了便于找到适用于各种不同应用的最佳材料，进行了越来越多的计算筛选工作。不幸的是，这些筛选研究大多局限于其最初的材料集，导致采用一种蛮力型的筛选方法。在这项工作中，我们提出了一种系统策略，该策略可以使用机器学习和进化算法，从超过100万亿种可能的极其多样且庞大的金属有机框架（MOF）集合中找到具有所需特性的MOF。结果表明，我们的算法能够发现964种甲烷工作容量超过200 cm³/cm³的MOF以及96种甲烷工作容量超过当前世界纪录208 cm³/cm³的MOF。我们相信，这种方法可以利用MOF的模块化性质，并且也能够很容易地扩展到其他重要应用中。
