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

doi:10.6023/A22010031

Abstract

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

Cite this article

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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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

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

	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

	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

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

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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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