Prediction of methane storage in covalent organic frameworks using big-data-mining approach

doi:10.1016/j.cjche.2021.03.002

中国化学工程学报 ›› 2021, Vol. 39 ›› Issue (11): 286-296.DOI: 10.1016/j.cjche.2021.03.002

• Resources and Environmental Technology • 上一篇下一篇

Prediction of methane storage in covalent organic frameworks using big-data-mining approach

Huan Zhang^1,2, Peisong Yang², Duli Yu^2,3, Kunfeng Wang³, Qingyuan Yang^1,2

1 State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China;
2 Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China;
3 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

收稿日期:2021-01-05 修回日期:2021-02-10 出版日期:2021-11-28 发布日期:2021-12-27
通讯作者: Kunfeng Wang, Qingyuan Yang
基金资助:
This work was financially supported by the National Natural Science Foundation of China (22078004), the Fundamental Research Funds for the Central Universities (buctrc201727) and the Big Science Project from BUCT (XK180301).

Prediction of methane storage in covalent organic frameworks using big-data-mining approach

Huan Zhang^1,2, Peisong Yang², Duli Yu^2,3, Kunfeng Wang³, Qingyuan Yang^1,2

1 State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China;
2 Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China;
3 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Received:2021-01-05 Revised:2021-02-10 Online:2021-11-28 Published:2021-12-27
Contact: Kunfeng Wang, Qingyuan Yang
Supported by:
This work was financially supported by the National Natural Science Foundation of China (22078004), the Fundamental Research Funds for the Central Universities (buctrc201727) and the Big Science Project from BUCT (XK180301).

摘要/Abstract

摘要： A combination of computational materials screening and machine learning (ML) technique is being adopted as a popular approach to study various materials toward application of interest. In this work, we began with high-throughput molecular simulations to calculate the methane storage (6.5 MPa) and deliverable (6.5-0.58 MPa) capacities of 404,460 covalent organic frameworks (COFs) at 298 K. Then, the full data sets with 23 features were randomly split into training and test sets in a ratio of 20:80, which were applied to evaluate the prediction abilities of several ML algorithms, including gradient boosting decision tree (GBDT), neural network (NN), support vector machine (SVM), random forest (RF) and decision tree (DT). The results indicate that the RF model has the highest prediction accuracy, which was further employed to reduce the dimension of features space and quantitatively analyze the relative importance of each feature value. The binary classification predictors built using the features with the highest influence weight can give a successful identification of top-performing candidates from the test set containing 323,168 COFs with an accuracy exceeding 96%. The deliverable capacities of the identified COFs were found to outperform those reported so far for various adsorbents. The findings may provide a useful guidance for the design and synthesis of new high-performance materials for methane storage application.

关键词: Covalent organic framework, Monte Carlo simulation, Methane, Machine learning, Model

Abstract: A combination of computational materials screening and machine learning (ML) technique is being adopted as a popular approach to study various materials toward application of interest. In this work, we began with high-throughput molecular simulations to calculate the methane storage (6.5 MPa) and deliverable (6.5-0.58 MPa) capacities of 404,460 covalent organic frameworks (COFs) at 298 K. Then, the full data sets with 23 features were randomly split into training and test sets in a ratio of 20:80, which were applied to evaluate the prediction abilities of several ML algorithms, including gradient boosting decision tree (GBDT), neural network (NN), support vector machine (SVM), random forest (RF) and decision tree (DT). The results indicate that the RF model has the highest prediction accuracy, which was further employed to reduce the dimension of features space and quantitatively analyze the relative importance of each feature value. The binary classification predictors built using the features with the highest influence weight can give a successful identification of top-performing candidates from the test set containing 323,168 COFs with an accuracy exceeding 96%. The deliverable capacities of the identified COFs were found to outperform those reported so far for various adsorbents. The findings may provide a useful guidance for the design and synthesis of new high-performance materials for methane storage application.

Key words: Covalent organic framework, Monte Carlo simulation, Methane, Machine learning, Model

Huan Zhang, Peisong Yang, Duli Yu, Kunfeng Wang, Qingyuan Yang. Prediction of methane storage in covalent organic frameworks using big-data-mining approach[J]. 中国化学工程学报, 2021, 39(11): 286-296.

Huan Zhang, Peisong Yang, Duli Yu, Kunfeng Wang, Qingyuan Yang. Prediction of methane storage in covalent organic frameworks using big-data-mining approach[J]. Chinese Journal of Chemical Engineering, 2021, 39(11): 286-296.

参考文献

[1] Energy Information Administration. Annual Energy Outlook 2020 with Projections to 2050. https://www.eia.gov/outlooks/aeo/.
[2] A. Schoedel, Z. Ji, O.M. Yaghi, The role of metal-organic frameworks in a carbon-neutral energy cycle, Nat. Energy 1(2016) 16034.
[3] K.J. Korman, G.E. Decker, M.R. Dworzak, M.M. Deegan, A.M. Antonio, G.A. Taggart, E.D. Bloch, Using low-pressure methane adsorption isotherms for higher-throughput screening of methane storage materials, ACS Appl. Mater. Interfaces 12(2020) 40318-40327.
[4] Y. Wu, D. Tang, R.J. Verploegh, H. Xi, D.S. Sholl, Impacts of gas impurities from pipeline natural gas on methane storage in metal-organic frameworks during long-term cycling, J. Phys. Chem. C. 121(2017) 15735-15745.
[5] H. Zhang, P. Deria, O.K. Farha, J.T. Hupp, R.Q. Snurr, A thermodynamic tank model for studying the effect of higher hydrocarbons on natural gas storage in metal-organic frameworks, Energy Environ. Sci. 8(2015) 1501-1510.
[6] T. Burchell, M. Rogers, Low pressure storage of natural gas for vehicular applications, SAE Trans. 109(2000) 2242-2246.
[7] Y. Peng, V. Krungleviciute, I. Eryazici, J.T. Hupp, O.K. Farha, T. Yildirim, Methane storage in metal-organic frameworks:Current records, surprise findings, and challenges, J. Am. Chem. Soc. 135(2013) 11887-11894.
[8] C.M. Simon, J. Kim, D.A. Gomez-Gualdron, J.S. Camp, Y.G. Chung, R.L. Martin, R. Mercado, M.W. Deem, D. Gunter, M. Haranczyk, D.S. Sholl, R.Q. Snurr, B. Smit, The materials genome in action:Identifying the performance limits for methane storage, Energy Environ. Sci. 8(2015) 1190-1199.
[9] J.A. Mason, M. Veenstra, J.R. Long, Evaluating metal-organic frameworks for natural gas storage, Chem. Sci. 5(2014) 32-51.
[10] M.E. Casco, M. Martínez-Escandell, E. Gadea-Ramos, K. Kaneko, J. SilvestreAlbero, F. Rodríguez-Reinoso, High-pressure methane storage in porous materials:are carbon materials in the pole position?, Chem Mater. 27(2015) 959-964.
[11] V.C. Menon, S. Komarneni, Porous adsorbents for vehicular natural gas storage:A review, J. Porous Mater. 5(1998) 43-58.
[12] P.D. Rolniak, R. Kobayashi, Adsorption of methane and several mixtures of methane and carbon dioxide at elevated pressures and near ambient temperatures on 5A and 13X molecular sieves by tracer perturbation chromatography, AIChE J. 26(1980) 616-625.
[13] Y. He, W. Zhou, G. Qian, B. Chen, Methane storage in metal-organic frameworks, Chem. Soc. Rev. 43(2014) 5657-5678.
[14] B. Liu, C. Sun, G. Chen, Molecular simulation studies of separation of CH₄/H₂ mixture in metal-organic frameworks with interpenetration and mixedligand, Chem. Eng. Sci. 66(2011) 3012-3019.
[15] B. Wang, L.-H. Xie, X. Wang, X.-M. Liu, J. Li, J.-R. Li, Applications of metal- organic frameworks for green energy and environment:New advances in adsorptive gas separation, storage and removal, Green Energy Environ. 3(2018) 191-228.
[16] B.M. Connolly, D.G. Madden, A.E.H. Wheatley, D. Fairen-Jimenez, Shaping the future of fuel:Monolithic metal-organic frameworks for high-density gas storage, J. Am. Chem. Soc. 142(2020) 8541-8549.
[17] J.A. Mason, J. Oktawiec, M.K. Taylor, M.R. Hudson, J. Rodriguez, J.E. Bachman, M.I. Gonzalez, A. Cervellino, A. Guagliardi, C.M. Brown, P.L. Llewellyn, N. Masciocchi, J.R. Long, Methane storage in flexible metal-organic frameworks with intrinsic thermal management, Nature 527(2015) 357-361.
[18] H.-M. Wen, B. Li, L. Li, R.B. Lin, W. Zhou, G. Qian, B. Chen, A metal-organic framework with optimized porosity and functional sites for high gravimetric and volumetric methane storage working capacities, Adv. Mater. 30(2018) 1704792.
[19] Z. Chen, P. Li, R. Anderson, X. Wang, X. Zhang, L. Robison, L.R. Redfern, S. Moribe, T. Islamoglu, D.A. Gómez-Gualdrón, T. Yildirim, J.F. Stoddart, O.K. Farha, Balancing volumetric and gravimetric uptake in highly porous materials for clean energy, Science 368(2020) 297-303.
[20] D. Alezi, Y. Belmabkhout, M. Suyetin, P.M. Bhatt, Ł.J. Weselin ′ ski, V. Solovyeva, K. Adil, I. Spanopoulos, P.N. Trikalitis, A.-H. Emwas, M. Eddaoudi, MOF crystal chemistry paving the way to gas storage needs:aluminum-based soc-MOF for CH₄, O₂, and CO₂ storage, J. Am. Chem. Soc. 137(2015) 13308-13318.
[21] C.S. Diercks, O.M. Yaghi, The atom, the molecule, and the covalent organic framework, Science 355(2017) eaal1585.
[22] L. Ascherl, T. Sick, J.T. Margraf, S.H. Lapidus, M. Calik, C. Hettstedt, K. Karaghiosoff, M. Döblinger, T. Clark, K.W. Chapman, F. Auras, T. Bein, Molecular docking sites designed for the generation of highly crystalline covalent organic frameworks, Nat. Chem. 8(2016) 310-316.
[23] T. Ma, E.A. Kapustin, S.X. Yin, L. Liang, Z. Zhou, J. Niu, L.-H. Li, Y. Wang, J. Su, J. Li, X. Wang, W.D. Wang, W. Wang, J. Sun, O.M. Yaghi, Single-crystal X-ray diffraction structures of covalent organic frameworks, Science 361(2018) 48- 52.
[24] Z. Li, T. He, Y. Gong, D. Jiang, Covalent organic frameworks:pore design and interface engineering, Acc. Chem. Res. 53(2020) 1672-1685.
[25] X. Guan, F. Chen, Q. Fang, S. Qiu, Design and applications of three dimensional covalent organic frameworks, Chem. Soc. Rev. 49(2020) 1357-1384.
[26] Z. Wang, S. Zhang, Y. Chen, Z. Zhang, S. Ma, Covalent organic frameworks for separation applications, Chem. Soc. Rev. 49(2020) 708-735.
[27] J. Huang, H. Xing, S. Yang, Y. Cao, C. Yuan, Y. Liu, J. Wang, Y. Cui, Microporous 3D covalent organic frameworks for liquid chromatographic separation of xylene isomers and ethylbenzene, J. Am. Chem. Soc. 141(2019) 8996- 9003.
[28] Y. Zeng, R. Zou, Y. Zhao, Carbon dioxide capture:Covalent organic frameworks for CO2 capture, Adv. Mater. 28(2016) 2855-2873.
[29] S. Yuan, X. Li, J. Zhu, G. Zhang, P. Van Puyvelde, B. der Bruggen, Covalent organic frameworks for membrane separation, Chem. Soc. Rev. 48(2019) 2665-2681.
[30] H. Furukawa, O.M. Yaghi, Storage of hydrogen, methane, and carbon dioxide in highly porous covalent organic frameworks for clean energy applications, J. Am. Chem. Soc. 131(2009) 8875-8883.
[31] J.L. Mendoza-Cortes, T.A. Pascal, W.A. Goddard, Design of covalent organic frameworks for methane storage, J. Phys. Chem. A. 115(2011) 13852-13857.
[32] J. Hu, J. Zhao, T. Yan, Methane uptakes in covalent organic frameworks with double halogen substitution, J. Phys. Chem. C. 119(2015) 2010-2014.
[33] A. Sharma, R. Babarao, N.V. Medhekar, A. Malani, Methane adsorption and separation in slipped and functionalized covalent organic frameworks, Ind. Eng. Chem. Res. 57(2018) 4767-4778.
[34] M. Tong, Y. Lan, Z. Qin, C. Zhong, Computation-ready, experimental covalent organic framework for methane delivery:Screening and material design, J. Phys. Chem. C. 122(2018) 13009-13016.
[35] Y.G. Chung, E. Haldoupis, B.J. Bucior, M. Haranczyk, S. Lee, H. Zhang, K.D. Vogiatzis, M. Milisavljevic, S. Ling, J.S. Camp, B. Slater, J.I. Siepmann, D.S. Sholl, R.Q. Snurr, Advances, updates, and analytics for the computation-ready, experimental metal-organic framework database:CoRE MOF 2019, J. Chem. Eng. Data. 64(2019) 5985-5998.
[36] M. Tong, Y. Lan, Q. Yang, C. Zhong, High-throughput computational screening and design of nanoporous materials for methane storage and carbon dioxide capture, Green Energy Environ. 3(2018) 107-119.
[37] C. Altintas, G. Avci, H. Daglar, A. Nemati Vesali Azar, S. Velioglu, I. Erucar, S. Keskin, Database for CO(2) Separation Performances of MOFs Based on Computational Materials Screening., ACS Appl. Mater. Interfaces. 10(2018) 17257-17268.
[38] P. Boyd, A. Chidambaram, E. García Diez, C. Ireland, T. Daff, R. Bounds, A. Gładysiak, P. Schouwink, S.M. Moosavi, M. Maroto-Valer, J. Reimer, J. Navarro, T. Woo, S. García, K. Stylianou, B. Smit, Data-driven design of metal-organic frameworks for wet flue gas CO₂ capture, Nature 576(2019) 253-256.
[39] R. Mercado, R.-S. Fu, A.V. Yakutovich, L. Talirz, M. Haranczyk, B. Smit, In silico design of 2D and 3D covalent organic frameworks for methane storage applications, Chem. Mater. 30(2018) 5069-5086.
[40] G.S. Fanourgakis, K. Gkagkas, E. Tylianakis, G.E. Froudakis, A universal machine learning algorithm for large-scale screening of materials, J. Am. Chem. Soc. 142(2020) 3814-3822.
[41] S.-Y. Kim, S.-I. Kim, Y.-S. Bae, Machine-learning-based prediction of methane adsorption isotherms at varied temperatures for experimental adsorbents, J. Phys. Chem. C. 124(2020) 19538-19547.
[42] X. Yuan, X. Deng, C. Cai, Z. Shi, H. Liang, S. Li, Z. Qiao, Machine learning and high-throughput computational screening of hydrophobic metal-organic frameworks for capture of formaldehyde from air, green, Energy Environ. (2020) 759-770.
[43] R. Anderson, J. Rodgers, E. Argueta, A. Biong, D.A. Gómez-Gualdrón, Role of pore chemistry and topology in the CO₂ capture capabilities of MOFs:From molecular simulation to machine learning, Chem. Mater. 30(2018) 6325-6337.
[44] Z. Gülsoy, K.B. Sezginel, A. Uzun, S. Keskin, R. Yıldırım, Analysis of CH₄ uptake over metal-organic frameworks using data-mining tools, ACS Comb. Sci. 21(2019) 257-268.
[45] M. Fernandez, N.R. Trefiak, T.K. Woo, Atomic property weighted radial distribution functions descriptors of metal-organic frameworks for the prediction of gas uptake capacity, J. Phys. Chem. C. 117(2013) 14095-14105.
[46] M. Pardakhti, P. Nanda, R. Srivastava, Impact of chemical features on methane adsorption by porous materials at varying pressures, J. Phys. Chem. C. 124(2020) 4534-4544.
[47] M. Tong, Y. Lan, Q. Yang, C. Zhong, Exploring the structure-property relationships of covalent organic frameworks for noble gas separations, Chem. Eng. Sci. 168(2017) 456-464.
[48] Y. Lan, X. Han, M. Tong, H. Huang, Q. Yang, D. Liu, X. Zhao, C. Zhong, Materials genomics methods for high-throughput construction of COFs and targeted synthesis, Nat. Commun. 9(2018) 5274.
[49] T.F. Willems, C.H. Rycroft, M. Kazi, J.C. Meza, M. Haranczyk, Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials, Microporous Mesoporous Mater. 149(2012) 134-141.
[50] M.G. Martin, J.I. Siepmann, Transferable potentials for phase equilibria. 1. United-atom description of n-alkanes, J. Phys. Chem. B. 102(1998) 2569-2577.
[51] S.L. Mayo, B.D. Olafson, W.A. Goddard, DREIDING:A generic force field for molecular simulations, J. Phys. Chem. 94(1990) 8897-8909.
[52] T.J.H. Vlugt, E. García-Pérez, D. Dubbeldam, S. Ban, S. Calero, Computing the heat of adsorption using molecular simulations:The effect of strong coulombic interactions, J. Chem. Theory Comput. 4(2008) 1107-1118.
[53] S. Haykin, Neural Networks:A Comprehensive Foundation, 1st ed., Prentice Hall PTR, USA, 1994.
[54] N. Cristianini, J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, Cambridge, UK, 2000.
[55] Y.-Y. Song, Y. Lu, Decision tree methods:Applications for classification and prediction, Shanghai Arch. Psychiatry. 27(2015) 130-135.
[56] T.G. Dietterich, An experimental comparison of three methods for constructing ensembles of decision trees:Bagging, boosting, and randomization, Mach. Learn. 40(2000) 139-157.
[57] L. Breiman, Random forests, Mach. Learn. 45(2001) 5-32.
[58] Y. Bengio, Y. Grandvalet, No unbiased estimator of the variance of K-fold crossvalidation, J. Mach. Learn. Res. 5(2004) 1089-1105.
[59] C.M. Simon, R. Mercado, S.K. Schnell, B. Smit, M. Haranczyk, What are the best materials to separate a xenon/krypton mixture?, Chem Mater. 27(2015) 4459- 4475.
[60] S. Wang, Comparative molecular simulation study of methane adsorption in metal organic frameworks, Energy Fuels 21(2007) 953-956.
[61] Y. Wu, H. Duan, H. Xi, Machine learning-driven insights into defects of zirconium metal-organic frameworks for enhanced ethane-ethylene separation, Chem. Mater. 32(2020) 2986-2997.
[62] H.M. El-Kaderi, J.R. Hunt, J.L. Mendoza-Cortés, A.P. Côté, R.E. Taylor, M. O'Keeffe, O.M. Yaghi, Designed synthesis of 3D covalent, Science 316(2007) 268-272.
[63] J.-M. Lin, C.-T. He, Y. Liu, P.-Q. Liao, D.-D. Zhou, J.-P. Zhang, X.-M. Chen, A Metal-organic framework with a pore size/shape suitable for strong binding and close packing of methane, Angew. Chemie Int. Ed. 55(2016) 4674-4678.

Prediction of methane storage in covalent organic frameworks using big-data-mining approach

Prediction of methane storage in covalent organic frameworks using big-data-mining approach

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