Singh Samrendra K, Sose Abhishek T, Wang Fangxi, Bejagam Karteek K, Deshmukh Sanket A
Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
J Chem Theory Comput. 2023 Oct 10;19(19):6686-6703. doi: 10.1021/acs.jctc.3c00081. Epub 2023 Sep 27.
Hydrogen gas (H) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.
氢气（H₂）是一种清洁的可再生能源，但缺乏高效且经济高效的存储材料是其广泛应用面临的一项挑战。金属有机框架（MOF）作为一类多孔材料，因其可调节的结构和化学特性，已被广泛研究用于氢气存储。然而，MOF提供的巨大设计空间使得选择或设计具有高氢气存储容量的合适MOF具有挑战性。为了克服这些挑战，我们提出了一种数据驱动的计算方法，该方法系统地设计用于氢气存储的新型功能化MOF。特别是，我们展示了一种混合粒子群优化集成遗传算法、巨正则蒙特卡罗（GCMC）模拟以及我们内部的MOF结构生成代码的框架，以设计具有优异氢气吸收性能的新型MOF。这个自动化的数据驱动框架向IRMOF - 10添加了合适的官能团以提高其氢气吸附能力。本文详细分析了顶级选择的MOF、它们的吸附等温线以及增强氢气吸附的MOF设计规则。我们发现一种功能化的IRMOF - 10，在1巴和77 K下，其增强后的氢气吸附量比纯IRMOF - 10增加了约6倍。此外，本研究还利用机器学习和深度学习技术分析了大量MOF结构和性质的数据集，以确定影响氢气吸附的关键因素。展示了基于所识别的MOF结构和化学性质使用机器学习/深度学习方法预测氢气吸附的概念验证。
