Citation: Cai Chengzhi, Li Lifeng, Deng Xiaomei, Li Shuhua, Liang Hong, Qiao Zhiwei. Machine Learning and High-throughput Computational Screening of Metal-organic Framework for Separation of Methane/ethane/propane[J]. Acta Chimica Sinica, ;2020, 78(5): 427-436. doi: 10.6023/A20030065 shu

Machine Learning and High-throughput Computational Screening of Metal-organic Framework for Separation of Methane/ethane/propane

  • Corresponding author: Qiao Zhiwei, zqiao@gzhu.edu.cn
  • † These authors contributed equally to this work.
  • Received Date: 13 March 2020
    Available Online: 16 April 2020

    Fund Project: the National Natural Science Foundation of China 21676094Project supported by the National Natural Science Foundation of China (Nos. 21978058, 21676094, 21576058) and the Natural Science Foundation of Guangdong Province (No. 2020A1515010800)the Natural Science Foundation of Guangdong Province 2020A1515010800the National Natural Science Foundation of China 21576058the National Natural Science Foundation of China 21978058

Figures(4)

  • In this work, the separation performance of methane/ethane/propane (C1, C2 and C3) mixture in the 137953 hypothetical metal-organic frameworks (MOFs) is calculated by high throughput computational screening and multiple machine learning (ML) algorithms. First, to avoid the competitive adsorption of water vapor, 31399 hydrophobic MOFs (hMOFs) were screened out. Then, grand canonical Monte Carlo (GCMC) simulations were employed to calculate the adsorption behavior of a mixture with a mole ratio of C1:C2:C3=7:2:1 in these hMOFs, respectively. Second, the relationships among six MOF structures/energy descriptors (the largest cavity diameter (LCD), void fraction (f), volumetric surface area (VSA), Henry coefficient (K), heat of adsorption (Qst), density of MOF (ρ)) and three performance indicators of MOFs (selectivities (S), adsorption capacities (N) of C1, C2, C3 and their trade-offs (TSN)) were established. The LCDs were calculated by Zeo++software, and VSAs were calculated using RASPA software using He and N2 as probes, respectively, and Qst and K were calculated in an infinite dilution of each gas molecule in an infinite dilution state using NVT-MC method in RASPA software. Then, we found that there existed the "second peaks" of N and S in part of structure-property relationships, and all the optimal MOFs located in the range of second peaks, especially for the separation of C1 or C2. Third, the above-mentioned six MOF descriptors and three MOF performance indicators were trained, tested and predicted by four ML algorithms, including decision tree, random forest (RF), support vector machine and Back Propagation neural network. Although the predictive effect for the selectivity was very low, the introduction of TSN can significantly improve the accuracy of ML prediction, especially for RF algorithm (R=0.99). Therefore, the RF was used to quantitatively analyze the relative importance of each MOF descriptor, and found that three descriptors (K, LCD and ρ) possessed the highest importance for the separation of C1 and C2, and three other descriptors (K, Qst and ρ) for the separation of C3. Moreover, three simple and clear paths of optimal MOFs for C1, C2 and C3 adsorption were designed by the decision tree model with the descriptors. Based on those paths, there were 96%, 85%, 95% probability that we can search for high-performance MOFs, respectively. Finally, the best 18 MOFs were identified for different separation applications of C1, C2 and C3. This study reveals the second peaks and key MOF descriptors governing the adsorption of light alkane, develops quantitative structure-property relationships by ML, and identifies the best adsorbents from a large collection of MOFs for the separation of C1, C2 and C3 from natural gas.
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    1. [1]

      Schoots, K.; Rivera-Tinoco, R.; Verbong, G.; van der Zwaan, B. Int. J. Greenhouse Gas Control. 2011, 5, 1614.  doi: 10.1016/j.ijggc.2011.09.008

    2. [2]

      Wu, F. F. M.S. Thesis, Tianjin University, Tianjin, 2014 (in Chinese).

    3. [3]

      Ravanchi, M. T.; Kaghazchi, T.; Kargari, A.; Soleimani, M. J. Taiwan Inst. Chem. Eng. 2009, 40, 511.  doi: 10.1016/j.jtice.2009.02.007

    4. [4]

      Xie, C. L.; Fang, Y. D. Petrochem. Ind. Technol. 2005, 12, 63.
       

    5. [5]

      Wu, D. M.S. Thesis, Tianjin University, Tianjin, 2012 (in Chinese).

    6. [6]

      Li, X. F.; Li, D. F. Petrochem. Technol. 2007, 36, 94.
       

    7. [7]

      Ma, Y. T.; Cong, S. G.; Hu, Y. F. Energy Chem. Ind. 2017, 38, 34.
       

    8. [8]

      Zhang, H.; Liu, Y. S.; Liu, W. H.; Zhang, D. X.; Zhai, H. Chem. Ind. Eng. Prog. 2007, 26, 95.
       

    9. [9]

      Yu, Q. Q. M.S. Thesis, Beijing University of Chemical Technology, Beijing, 2016 (in Chinese).

    10. [10]

      Li, S. Z. M.S. Thesis, Harbin Institute of Technology, Harbin, 2011 (in Chinese).

    11. [11]

      Wu, X. J.; Zhao, P.; Fang, J. M.; Wang, J.; Liu, B. S.; Cai, W. Q. Acta Phys.-Chim. Sin. 2014, 30, 2043.  doi: 10.3866/PKU.WHXB201409222

    12. [12]

      Zhou, J. H.; Zhao, H. L.; Hu, J.; Liu, H. L.; Hu, Y. CIESC J. 2014, 65, 1680.  doi: 10.3969/j.issn.0438-1157.2014.05.018

    13. [13]

      Zhu, G. F.; Chen, L. T.; Cheng, G. H.; Zhao, J.; Yang, C.; Zhang, Y. Z.; Wang, X.; Fan, J. Acta Chim. Sinica 2019, 77, 434.
       

    14. [14]

      Fu, J.; Zhou, G. Y.; Hou, Z. Y.; Tian, H. C.; Xia, C. M.; Zhang, W.; Liu, J. T.; Wu, J. L.; Zhao, J. D.; Cang, X. L. Opt. Laser Technol. 2017, 91, 22.  doi: 10.1016/j.optlastec.2016.11.027

    15. [15]

      Liu, M. L.; Wu, Q.; Shi, H. F.; An, Z. F.; Huang, W. Acta Chim. Sinica 2018, 76, 246.
       

    16. [16]

      Cardenal, A. D.; Park, H. J.; Chalker, C. J.; Ortiz, K. G.; Powers, D. C. Chem. Commun. 2017, 53, 7377.  doi: 10.1039/C7CC02570J

    17. [17]

      Meng, S. Y.; Wang, M. M.; Lu, B. L.; Xue, Q. J.; Yang, Z. W. Acta Chim. Sinica 2019, 77, 1184.  doi: 10.7503/cjcu20180709
       

    18. [18]

      Wu, Z. M.; Shi, Y.; Li, C. Y.; Niu, D. Y.; Chu, Q.; Xiong, W.; Li, X. Y. Acta Chim. Sinica 2019, 77, 758.
       

    19. [19]

      Liu, R. X.; He, X. Y.; Niu, L. T.; Lv, B. L.; Yu, F.; Zhang, Z.; Yang, Z. W. Acta Chim. Sinica 2019, 77, 653.
       

    20. [20]

      Cao, L. Y.; Wang, T. T.; Wang, C. Chin. J. Chem. 2018, 36,

    21. [21]

      Zou, Z.; Li, S. Q.; He, D. G.; He, X. X.; Wang, K. M.; Li, L. L.; Yang, X.; Li, H. F. J. Mater. Chem. B 2017, 5, 2126.  doi: 10.1039/C6TB03379B

    22. [22]

      Couck, S.; Van Assche, T. R.; Liu, Y. Y.; Baron, G. V.; Van Der Voort, P.; Denayer, J. F. Langmuir 2015, 31, 5063.  doi: 10.1021/acs.langmuir.5b00655

    23. [23]

      Ponraj, Y. K.; Borah, B. J. Mol. Graph. Model. 2020, 97, 107574.  doi: 10.1016/j.jmgm.2020.107574

    24. [24]

      Tang, Y. N.; Wang, S.; Zhou, X.; Wu, Y.; Xian, S. K.; Li, Z. Chem. Eng. Sci. 2020, 213, 115355.  doi: 10.1016/j.ces.2019.115355

    25. [25]

      Fan, W. D.; Wang, X.; Zhang, X. R.; Liu, X. P.; Wang, Y. T.; Kang, Z. X.; Dai, F. N.; Xu, B.; Wang, R. M.; Sun, D. F. ACS Central. Sci. 2019, 5, 1261.  doi: 10.1021/acscentsci.9b00423

    26. [26]

      Chen, Y. W.; Qiao, Z. W.; Lv, D. F.; Wu, H. X.; Shi, R. F.; Xia, Q. B.; Wang, H. H.; Zhou, J.; Li, Z. Ind. Eng. Chem. Res. 2017, 56, 4488.  doi: 10.1021/acs.iecr.6b05010

    27. [27]

      Guo, W. J.; Yu, J.; Dai, Z.; Hou, W. Z. Acta Chim. Sinica 2019, 77, 1203.  doi: 10.11862/CJIC.2019.142
       

    28. [28]

      Wang, X.; Zhang, Y.; Chang, Z.; Huang, H.; Liu, X. T.; Xu, J. L.; Bu, X. H. Chin. J. Chem. 2019, 37, 871.  doi: 10.1002/cjoc.201900247

    29. [29]

      Qiao, W. Z.; Song, T. Q.; Zhao, B. Chin. J. Chem. 2019, 37, 474.  doi: 10.1002/cjoc.201800587

    30. [30]

      Chen, Z. Y.; Liu, J. W.; Cui, H.; Zhang, L.; Su, C. Y. Acta Chim. Sinica 2019, 77, 242.  doi: 10.3969/j.issn.0253-2409.2019.02.014
       

    31. [31]

      Zeng, J. Y.; Wang, X. S.; Zhang, X. Z.; Zhuo, R. X. Acta Chim. Sinica 2019, 77, 1156.
       

    32. [32]

      Liu, Z. L.; Li, W.; Liu, H.; Zhuang, X. D.; Li, S. Acta Chim. Sinica 2019, 77, 323.  doi: 10.11862/CJIC.2019.034
       

    33. [33]

      Bian, L.; Li, W.; Wei, Z. Z.; Liu, X. W.; Li, S. Acta Chim. Sinica 2018, 76, 303.  doi: 10.3866/PKU.WHXB201708302
       

    34. [34]

      Lan, Y. S.; Han, X. H.; Tong, M. M.; Huang, H. L.; Yang, Q. Y.; Liu, D. H.; Zhao, X.; Zhong, C. L. Nat. Commun. 2018, 9, 5274.  doi: 10.1038/s41467-018-07720-x

    35. [35]

      Qiao, Z. W.; Xu, Q. S.; Jiang, J. W. J. Mater. Chem. A 2018, 6, 18898.  doi: 10.1039/C8TA04939D

    36. [36]

      Wu, X. J.; Zheng, J.; Li, J.; Cai, W. Q. Acta Phys.-Chim. Sin. 2013, 29, 2207.  doi: 10.3866/PKU.WHXB201307191

    37. [37]

      Li, W.; Xia, X. X.; Cao, M.; Li, S. J. Mater. Chem. A 2019, 7, 7470.  doi: 10.1039/C8TA07909A

    38. [38]

      Shi, Z. N.; Yang, W. Y.; Deng, X. M.; Cai, C. Z.; Yan, Y. L.; Liang, H.; Liu, Z. L.; Qiao, Z. W. Mol. Syst. Des. Eng. 2020, DOI:10.1039/d0me00005a.  doi: 10.1039/d0me00005a

    39. [39]

      Moghadam, P. Z.; Rogge, S. M. J.; Li, A.; Chow, C.-M.; Wieme, J.; Moharrami, N.; Aragones-Anglada, M.; Conduit, G.; Gomez-Gualdron, D. A.; Van Speybroeck, V.; Fairen-Jimenez, D. Matter 2019, 1, 219.  doi: 10.1016/j.matt.2019.03.002

    40. [40]

      Fernandez, M.; Woo, T. K.; Wilmer, C. E.; Snurr, R. Q. J. Phys. Chem. C 2013, 117, 7681.  doi: 10.1021/jp4006422

    41. [41]

      Shah, M. S.; Tsapatsis, M.; Siepmann, J. I. Angew. Chem. 2016, 128, 6042.  doi: 10.1002/ange.201600612

    42. [42]

      Breiman, L. I.; Friedman, J. H.; Olshen, R. A.; Stone, C. J. Encycl. Ecol. 1984, 40, 358.

    43. [43]

      Breiman, L. Mach. Learn. 2001, 45, 5.  doi: 10.1023/A:1010933404324

    44. [44]

      Raccuglia, P.; Elbert, K. C.; Adler, P. D. F.; Falk, C.; Wenny, M. B.; Mollo, A.; Zeller, M.; Friedler, S. A.; Schrier, J.; Norquist, A. J. Nature 2016, 533, 73.  doi: 10.1038/nature17439

    45. [45]

      Zhang, W. G.; Goh, A. T. C. Geosci. Front. 2014, 7, 45.

    46. [46]

      Wu, X. J.; Xiang, S. C.; Su, J. Q.; Cai, W. Q. J. Phys. Chem. C 2019, 123, 8550.

    47. [47]

      Wang, X.; Zhang, X. R.; Zhang, K.; Wang, X. K.; Wang, Y. T.; Fan, W. D.; Dai, F. N. Inorg. Chem. Front. 2019, 6, 1152.  doi: 10.1039/C8QI01404C

    48. [48]

      Llewellyn, P. L.; Horcajada, P.; Maurin, G.; Devic, T.; Rosenbach, N.; Bourrelly, S.; Serre, C.; Vincent, D.; Loera-Serna, S.; Filinchuk, Y.; Férey, G. J. Am. Chem. Soc. 2009, 131, 13002.  doi: 10.1021/ja902740r

    49. [49]

      Wilmer, C. E.; Farha, O. K.; Yildirim, T.; Eryazici, I.; Krunglevi-ciute, V.; Sarjeant, A. A.; Snurr, R. Q.; Hupp, J. T. Energy Environ. Sci. 2013, 6, 1158.  doi: 10.1039/c3ee24506c

    50. [50]

      Wilmer, C. E.; Leaf, M.; Lee, C. Y.; Farha, O. K.; Hauser, B. G.; Hupp, J. T.; Snurr, R. Q. Nat. Chem. 2012, 4, 83.  doi: 10.1038/nchem.1192

    51. [51]

      Rappé, A. K.; Casewit, C. J.; Colwell, K. S.; III, W. A. G.; Skiff, W. M. J. Am. Chem. Soc. 1992, 114, 10024.  doi: 10.1021/ja00051a040

    52. [52]

      Martin, G. M.; Siepmann, J. I. J. Phys. Chem. B 1998, 102, 2569.  doi: 10.1021/jp972543+

    53. [53]

      Horn, H. W.; Swope, W. C.; Pitera, J. W.; Madura, J. D.; Head-Gordon, T. J. Chem. Phys. 2004, 120, 9665.
       

    54. [54]

      Kadantsev, E. S.; Boyd, P. G.; Daff, T. D.; Woo, T. K. J. Phys. Chem. Lett. 2013, 4, 3056.  doi: 10.1021/jz401479k

    55. [55]

      Willems, T. F.; Rycroft, C. H.; Kazi, M.; Meza, J. C.; Haranczyk, M. Microporous Mesoporous Mater. 2012, 149, 134.  doi: 10.1016/j.micromeso.2011.08.020

    56. [56]

      Dubbeldam, D.; Calero, S.; Ellis, D. E.; Snurr, R. Q. Mol. Simul. 2015, 42, 81.

    57. [57]

      Moghadam, P. Z.; Fairen-Jimenez, D.; Snurr, R. Q. J. Mater. Chem. A 2016, 4, 529.  doi: 10.1039/C5TA06472D

    58. [58]

      Ewald, P. P. Ann. Phys. 2006, 369, 253.
       

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