Machine learning-assisted high-throughput computational screening of MOFs and advances in gas separation research

doi:10.11949/0438-1157.20241229

Abstract

Abstract:

Metal organic frameworks (MOFs) have garnered extensive research interest in fields such as gas storage, adsorption separation, and catalysis due to their high surface area, large pore volume, and tunable structures. In recent years, the surge in the number of MOFs has posed unprecedented challenges in finding the ideal MOF for specific applications. In this scenario, high-throughput computational screening (HTCS) has become the most effective research method for screening high-performance target MOFs from a vast array of materials. HTCS will generate a large amount of multidimensional data, which can be further used for machine learning (ML) training. Recently, applying ML to HTCS of MOFs has become a new hotspot, which can not only reveal the potential structure-performance relationships of materials but also provide insights into their performance trends in different applications, especially in gas storage and separation. In this review, we highlight the latest advances in ML-assisted HTCS in the field of MOFs gas separation, systematically analyze the internal mechanism of ML and HTCS collaboration to improve screening efficiency in the search for high-performance MOFs, and explore the opportunities and challenges presented in this new field.

Key words: metal organic frameworks, high-throughput computational screening, molecular simulation, machine learning, adsorption and separation

摘要：

金属有机框架（metal organic frameworks，MOFs）材料，以其高比表面积、大孔体积以及结构可调等特性，在气体储存、吸附分离以及催化等诸多领域引起了广泛关注。近年来MOFs的数量呈现爆发式增长态势，这使得针对特定应用场景探寻合适的MOFs成为一项极具挑战性的任务。在此情形下，高通量计算筛选（high-throughput computational screening，HTCS）成为从海量材料中筛选出高性能目标MOFs最为有效的研究方法。HTCS会产生大量多维的数据，而这些数据可进一步用于机器学习（machine learning，ML）训练。最近，将ML应用到MOFs的HTCS中成为新的热点，它不仅可以揭示材料潜在的结构-性能关系，还可以洞悉它们在不同应用中的性能变化，尤其是在气体储存和分离方面。本综述着重介绍了ML辅助HTCS在MOFs气体分离领域的最新技术进展，系统分析了在探寻高性能MOFs时ML与HTCS相互协同以提升筛选效率的内在机制，深入探讨了在这一新领域中呈现出的机遇和挑战。

关键词: 金属有机框架, 高通量计算筛选, 分子模拟, 机器学习, 吸附分离

CLC Number:

TQ 028.8

Jialang HU, Mingyuan JIANG, Lyuming JIN, Yonggang ZHANG, Peng HU, Hongbing JI. Machine learning-assisted high-throughput computational screening of MOFs and advances in gas separation research[J]. CIESC Journal, 2025, 76(5): 1973-1996.

胡嘉朗, 姜明源, 金律铭, 张永刚, 胡鹏, 纪红兵. 机器学习辅助MOFs高通量计算筛选及气体分离研究进展[J]. 化工学报, 2025, 76(5): 1973-1996.

Figures/Tables 13

Fig.1 Chart of statistics of the literature and timeline: (a) The literature statistics when using “machine learning” as a keyword; (b) The literature statistics when using “metal organic framework” as a keyword; (c) The statistics of the literature when using “machine learning” and “metal organic framework” as keywords; (d) A timeline of major milestones in computational MOFs research[35， 42-44]

Fig.2 Schematic illustration of the CoRE MOF database construction[49]

Table 1 Classification of ML algorithms

方法		描述
监督学习	集成方法	集成方法是一种机器学习策略，通过组合多个模型的预测结果来提高整体性能和泛化能力
	神经网络	神经网络是一种模仿人脑神经元工作方式的机器学习算法，广泛用于分类、回归和生成任务
	朴素贝叶斯	朴素贝叶斯算法是一种基于贝叶斯定理的分类算法，假设特征之间相互独立
	支持向量机	支持向量机是一种用于分类和回归分析的监督学习算法
	随机森林	随机森林是一种使用多棵决策树进行分类和回归的集成学习算法
	k-近邻	k-近邻算法是一种基于实例的学习方法，通过计算样本之间的距离对数据进行分类
	决策树	决策树是一种具有树状结构的分类器，它根据一系列规则对数据进行分类
	极端梯度提升	极端梯度提升通过逐步添加弱学习器（通常是决策树）优化模型性能
半监督学习	自训练算法	自训练算法主要用于利用未标记数据提升模型性能
	基于图的半监督算法	基于图的半监督学习算法是一类利用图结构来表示数据及其标签信息的方法
	半监督支持向量机	半监督支持向量机是一种结合标记和未标记数据的分类方法，旨在通过最大化分类间隔来提升学习效果
无监督学习	k均值聚类	k均值聚类是一种常用的无监督学习算法，主要用于将数据集分成k个簇，使得每个簇内的样本相似度高而不同簇之间的样本相似度低
	层次聚类	层次聚类用于将数据点按照相似度进行分组，形成一个树状的层次结构（聚类树或树状图）
	自编码器	自编码器是一种神经网络，主要用于数据降维和特征提取
	主成分分析	主成分分析是一种用于数据降维的算法，通过提取数据中的主成分来简化复杂数据集
	高斯混合模型	高斯混合模型是一种基于概率的聚类算法，适用于建模复杂数据分布
强化学习	Q学习	Q学习基于价值迭代的方法，能够在不知道环境动态的情况下进行学习
强化学习	时间差分学习	时间差分学习通过结合蒙特卡罗方法和动态规划的思想来更新价值估计

Table 1 Classification of ML algorithms

方法		描述
监督学习	集成方法	集成方法是一种机器学习策略，通过组合多个模型的预测结果来提高整体性能和泛化能力
	神经网络	神经网络是一种模仿人脑神经元工作方式的机器学习算法，广泛用于分类、回归和生成任务
	朴素贝叶斯	朴素贝叶斯算法是一种基于贝叶斯定理的分类算法，假设特征之间相互独立
	支持向量机	支持向量机是一种用于分类和回归分析的监督学习算法
	随机森林	随机森林是一种使用多棵决策树进行分类和回归的集成学习算法
	k-近邻	k-近邻算法是一种基于实例的学习方法，通过计算样本之间的距离对数据进行分类
	决策树	决策树是一种具有树状结构的分类器，它根据一系列规则对数据进行分类
	极端梯度提升	极端梯度提升通过逐步添加弱学习器（通常是决策树）优化模型性能
半监督学习	自训练算法	自训练算法主要用于利用未标记数据提升模型性能
	基于图的半监督算法	基于图的半监督学习算法是一类利用图结构来表示数据及其标签信息的方法
	半监督支持向量机	半监督支持向量机是一种结合标记和未标记数据的分类方法，旨在通过最大化分类间隔来提升学习效果
无监督学习	k均值聚类	k均值聚类是一种常用的无监督学习算法，主要用于将数据集分成k个簇，使得每个簇内的样本相似度高而不同簇之间的样本相似度低
	层次聚类	层次聚类用于将数据点按照相似度进行分组，形成一个树状的层次结构（聚类树或树状图）
	自编码器	自编码器是一种神经网络，主要用于数据降维和特征提取
	主成分分析	主成分分析是一种用于数据降维的算法，通过提取数据中的主成分来简化复杂数据集
	高斯混合模型	高斯混合模型是一种基于概率的聚类算法，适用于建模复杂数据分布
强化学习	Q学习	Q学习基于价值迭代的方法，能够在不知道环境动态的情况下进行学习
强化学习	时间差分学习	时间差分学习通过结合蒙特卡罗方法和动态规划的思想来更新价值估计

Fig.3 (a) Probabilities of different metals being in the selected MOF subset (based on WSRD); (b) Probability of different metal types being in the selected MOF subset. (the inner circle indicates the TOP1000 MOF set, and the outer circle indicates all MOFs); (c) Probability of different transition metals being in the selected subset[67]; (d) Flowchart of ML-assisted molecular simulation for high-throughput screening of high-performance desired bio-MOFs for O2/N2 adsorption separation[68]

Fig.4 Flowchart of ML for the top MOF materials for CH4/N2 separation[72]

Fig.5 (a) RI of different descriptors for different levels of MOFs datasets at all levels in the CH4/CO2 system[76]; (b) Architecture of the artificial neural networks[77]

Fig.6 (a) Schematic illustrations of the chemical descriptors for training ML models to predict CO2/CO selectivities and adsorption uptakes; (b) Schematic illustration of the correlations among CO2/CO selectivity, features, and CO2 (left) or CO (right) loading; (c) Correlation heatmap between features and adsorption properties[85]

Fig.7 (a) Schematic illustrations of model structure framework for GC-Trans[89]; (b) Three-level DT classifiers of MOFs with high CO2 and N2 uptake capacities[90]

Fig.8 Schematic workflow of the ML-assisted discovery of the top-performing C3H8-selective MOFs from the CoRE MOF database[93]

Fig.9 Schematic workflow of the ML-assisted discovery of the high-performing for one-step separation of C2H2/C2H4/C2H6[38]

Fig.10 (a) Flowchart of ML for the top MOF materials for SF6/N2 separation[37]; (b) Schematic of GCMC and ML calculation steps[98]

Fig.11 (a) Schematic of GCMC and ML calculation workflow[105]; (b) Workflow of screening and assembly for promising MOFs on Kr/Xe separation[106]

Fig.12 (a) Schematic of GCMC and ML calculation steps[115]; (b) Comparison of the separation performance for the identified MOF with various porous materials under the condition of 1.0 bar[116]

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