机器学习与分子模拟协同的CH4/H2分离金属有机框架高通量计算筛选

doi:10.6023/A22010031

摘要/Abstract

在减少CO₂排放、实现碳中和的背景下, 金属有机框架(MOFs)在清洁能源领域展现出广阔应用前景. 提出一种机器学习和分子模拟协同的分层筛选策略, 快速、准确地从134185个假设MOFs中识别出具有最佳CH₄/H₂分离性能的吸附剂. 首先, 根据MOFs的结构性质, 筛掉孔径或体积比表面积不恰当的吸附剂, 初筛后MOFs的数量减至62278个. 接下来, 抽取10% MOFs将结构和化学混合描述符作为特征, 利用随机森林分别构建变压吸附和真空变压吸附过程中其对CH₄的吸附剂性能得分(APS)预测模型. 相比于其他模型构建策略, 基于本策略构建的模型具有最优预测准确性, 可用于余下MOFs的性能预测. 随后根据APS预测值排序, 筛选出Top 1000的MOFs并利用分子模拟修正预测结果, 进一步确定了10个最佳MOFs. 最后, 对描述符的重要性进行解释, 揭示了实现模型在不同操作场景下的迁移具有潜力, 为未来开发适用于多操作场景下的高性能MOFs筛选方法提供了一条高效的研究路径和方法.

关键词: 金属有机框架, CH₄/H₂分离, 分子模拟, 机器学习, 可解释性

In this work, a hierarchical screening strategy by synergizing machine learning (ML) and molecular simulation was proposed to identify the optimal adsorbents for CH₄/H₂ separation from 134185 hypothetical metal-organic frameworks (MOFs). At the initial screening, MOF materials with inappropriate pore size and/or volumetric surface area were removed from the total database, resulting in a list of 62278 MOFs. Among them, 10% MOFs were randomly chosen and grand canonical Monte Carlo (GCMC) simulations were performed to calculate the adsorption behaviors of CH₄/H₂ mixture in these MOFs under vacuum swing adsorption (VSA) and pressure swing adsorption (PSA) conditions. Following this, structural/ chemical descriptors and corresponding adsorbent performance scores (APS) of the selected MOFs were employed to develop the random forest (RF) models for VSA and PSA processes. Compared with the accuracy of other ML algorithms, covering support vector machine, k-nearest neighbor, decision tree, and artificial neural network, the proposed model exhibits the optimum predictive power. Meanwhile, the hybrid of structural and chemical descriptors, as well as the application of the preliminary screening strategy improve the accuracy of the RF model. Thus, it was used to predict the APS values of the remaining 90% MOFs in the next stage of screening, and the top 1000 candidates were screened out according to the results. GCMC simulations were subsequently carried out on the top candidates to refine the predictions, and then ten MOFs with the best CH₄/H₂ separation performance were obtained under VSA and PSA conditions, respectively. The high performance of the optimal MOFs was verified by comparison with well-studied MOF materials in the literature. Finally, the feature importance of the descriptors was interpreted by the Shapley Additive Explanations. The result reveals the potential for the developed model to transfer between the two operating conditions due to the consistency of the dominant descriptors, which also provides an efficient pathway for rapid screening of promising MOF adsorbents in CH₄/H₂ separation suitable for different operation scenarios.

Key words: metal-organic frameworks, CH₄/H₂ separation, molecular simulation, machine learning, interpretability

引用此文

王诗慧, 薛小雨, 程敏, 陈少臣, 刘冲, 周利, 毕可鑫, 吉旭. 机器学习与分子模拟协同的CH₄/H₂分离金属有机框架高通量计算筛选[J]. 化学学报, 2022, 80(5): 614-624.

Shihui Wang, Xiaoyu Xue, Min Cheng, Shaochen Chen, Chong Liu, Li Zhou, Kexin Bi, Xu Ji. High-Throughput Computational Screening of Metal-Organic Frameworks for CH₄/H₂ Separation by Synergizing Machine Learning and Molecular Simulation[J]. Acta Chimica Sinica, 2022, 80(5): 614-624.

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图/表 11

Category	Descriptor	Unit
Structural Descriptor	largest cavity diameter (LCD)	nm
	pore limiting diameter (PLD)	nm
	gravimetric surface area (GSA)	m²/g
	volumetric surface area (VSA)	m²/cm³
	crystal density (Density)	g/cm³
	helium void fraction (HVF)	—
Chemical Descriptor	no. of H atoms (Num H)	—
	no. of C atoms (Num C)	—
	no. of N atoms (Num N)	—
	no. of O atoms (Num O)	—
	total degree of unsaturation (TDU)	[(no. of C atoms×2) + 2- no. of H atoms]/2
	metallic percentage (MP)	(no. of metal atoms/ no. of C atoms)×100
	electronegative to total ratio (ET ratio)	(no. of electronegative atoms)/ (no. of atoms)
	weighted electronegativity per atom (WEA)	(sum of weighted ^a electronegative atoms)/ (no. of atoms)
	Henry’s coefficient of methane (HC methane)	mol•kg^–1•Pa^–1
	Henry’s coefficient of hydrogen (HC hydrogen)	mol•kg^–1•Pa^–1

表1. MOF的描述符汇总

Table 1. Summarization of MOF descriptors

图1. ML方法和GCMC模拟协同的分层筛选策略示意图

Figure 1. Flowsheet diagram for the proposed hierarchical screening strategy by synergizing ML and GCMC

图2. 在VSA和PSA操作条件下APS值分别与MOFs的(a), (c) LCD和(b), (d) VSA关系

Figure 2. APS value of MOFs as functions of (a), (c) LCD and (b), (d) VSA under VSA and PSA operation conditions

	VSA			PSA
Combination^a	Validation set R²	Test set R²	Test set RMSE	Validation set R²	Test set R²	Test set RMSE
RF+CD	0.879	0.910	16.658	0.814	0.842	25.481
RF+SD	0.423	0.441	57.967	0.498	0.527	48.229
RF+SC	0.935	0.937	13.386	0.896	0.907	20.345
RF+SC (未初筛)	0.833	0.707	46.124	0.868	0.869	22.041
SVM+SC	0.816	0.802	28.446	0.827	0.808	29.383
KNN+SC	0.721	0.759	31.306	0.754	0.759	32.907
DT+SC	0.891	0.929	16.946	0.808	0.834	27.297
ANN+SC	0.816	0.819	25.225	0.880	0.823	28.207

表2. 在VSA和PSA操作条件下, 基于不同策略构建的ML模型性能指标

Table 2. Performance metrics of models constructed by different methods and descriptor combinations under VSA and PSA conditions

图3. 基于结构/化学混合描述符构建的RF模型分别在VSA和PSA操作条件下的(a), (b)训练集和(c), (d)测试集APS预测值和模拟值的对比

Figure 3. Comparisons of APS predicted values and APS simulated values of (a), (b) training set and (c), (d) test set using structural and chemical variables applied on RF models under VSA and PSA operation conditions

图4. (a) VSA和(b) PSA操作条件下Top1000 MOFs的APS预测值和模拟值对比

Figure 4. Comparisons of APS predicted values and APS simulated values of Top1000 MOFs under (a) VSA and (b) PSA operation conditions

图5. 在VSA操作条件下, Top 1000 MOFs、Top10 MOFs以及Tong等[59]提出的常用MOFs的CH4/H2选择性和工作能力关系

Figure 5. Relationships between selectivity and working capacity of Top1000 MOFs, Top10 MOFs selected in this study and commonly used MOFs proposed by Tong et al. [59] under VSA operation condition

图6. 在PSA操作条件下, Top 1000 MOFs、Top10 MOFs、Altintas 等[30]筛选出Top20 MOFs、Tong等[59]提出的常用MOFs的CH4/H2选择性和工作能力关系

Figure 6. Relationships between selectivity and working capacity of Top1000 MOFs, Top10 MOFs selected in this study and Top 20 MOFs provided by Altintas et al.[30], as well as commonly used MOFs proposed by Tong et al. [59] under PSA operation condition

VSA					PSA
MOF ID	S_CH4/H2	ΔN_CH4/(mol•kg^–1)	APS/(mol•kg^–1)	R%	MOF ID	S_CH4/H2	ΔN_CH4/(mol•kg^–1)	APS/(mol•kg^–1)	R%
hMOF-5073958	194.7	3.49	679.3	81.87	hMOF-5074069	47.5	6.65	315.8	80.25
hMOF-5079271	187.0	3.60	673.9	82.45	hMOF-27591	43.8	6.96	305.0	80.85
hMOF-36375	203.0	3.28	666.6	82.06	hMOF-27683	40.9	7.38	301.5	82.05
hMOF-5055186	172.2	3.80	654.3	81.62	hMOF-5056488	40.9	7.23	295.7	81.01
hMOF-26377	210.8	3.07	647.7	80.25	hMOF-5071338	44.0	6.69	294.9	80.58
hMOF-5073994	197.4	3.23	637.8	82.59	hMOF-5046827	44.6	6.60	294.1	81.11
hMOF-25357	214.9	2.94	630.8	82.00	hMOF-29743	40.3	7.09	285.5	82.45
hMOF-5056768	164.9	3.71	611.0	81.51	hMOF-5071184	46.7	6.00	280.3	80.11
hMOF-5081782	179.1	3.37	604.6	83.54	hMOF-32836	36.1	7.61	274.9	83.83
hMOF-26209	212.0	2.83	599.8	81.82	hMOF-29330	42.6	6.31	268.4	80.01

表3. 在VSA和PSA条件下, 根据R%>80%和APS值排名的Top10 MOFs

Table 3. Top 10 MOFs ranked on combined metrics of R% and APS values under VSA and PSA conditions

图7. 在VSA和PSA操作条件下, 结构/化学混合描述符对APS值的相对重要度

Figure 7. The importance of structural/chemical descriptors for APS value of MOFs under VSA and PSA operation conditions

图8. 在VSA和PSA操作条件下, 结构/化学混合描述符对于CH4/H2选择性和工作能力的重要度

Figure 8. The importance of structural/chemical descriptors for selectivity and working capacity under VSA and PSA operation conditions

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机器学习与分子模拟协同的CH₄/H₂分离金属有机框架高通量计算筛选

High-Throughput Computational Screening of Metal-Organic Frameworks for CH₄/H₂ Separation by Synergizing Machine Learning and Molecular Simulation

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