金属有机骨架
节点(物理)
土壤孔隙空间特征
蒙特卡罗方法
功能(生物学)
人工神经网络
计算机科学
拓扑(电路)
材料科学
化学
机器学习
数学
多孔性
吸附
有机化学
工程类
进化生物学
生物
统计
组合数学
复合材料
结构工程
作者
Xiangyu Zhang,Kexin Zhang,Hyeonsuk Yoo,Yongjin Lee
出处
期刊:ACS Sustainable Chemistry & Engineering
[American Chemical Society]
日期:2021-02-05
卷期号:9 (7): 2872-2879
被引量:34
标识
DOI:10.1021/acssuschemeng.0c08806
摘要
This paper presents a computational study to design tailor-made metal–organic frameworks (MOFs) for efficient CO2 capture in humid conditions. Target-specific MOFs were generated in our computational platform incorporating the Monte Carlo tree search and recurrent neural networks according to the objective function values that combine three requirements of high adsorption performance, experimental accessibility of designed materials, and good hydrophobicity (i.e., the low Henry coefficient of water in pore space) to be applied in humid conditions. With a given input of 27 different combinations of metal node and topology net information extracted from experimental MOFs, our approach successfully designed promising and novel metal–organic frameworks for CO2 capture, satisfying the three requirements in good balance. Furthermore, the detailed analysis of the structure–property relationship identified that moderate Di (the diameter of the largest included sphere) of 14.18 Å and accessible surface area (ASA) of 1750 m2/g values are desirable for high-performing MOFs for CO2 capture, which is attributed to the trade-off relationship between good adsorption selectivity (small pore size is desired) and high adsorption capacity (sufficient pore size is necessary).
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