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Haoyu Yin, Muzi Xu, Zhiyao Luo, Xiaotian Bi, Jiali Li, Sui Zhang, Xiaonan Wang. Machine learning for membrane design and discovery. Green Energy&Environment, 2024, 9(1): 54-70. doi: 10.1016/j.gee.2022.12.001
Citation: Haoyu Yin, Muzi Xu, Zhiyao Luo, Xiaotian Bi, Jiali Li, Sui Zhang, Xiaonan Wang. Machine learning for membrane design and discovery. Green Energy&Environment, 2024, 9(1): 54-70. doi: 10.1016/j.gee.2022.12.001
shu

Machine learning for membrane design and discovery

doi: 10.1016/j.gee.2022.12.001

  • a. Department of Chemical Engineering, Tsinghua University, 100084, Beijing, China;

  • b. School of Materials Science & Engineering, Tsinghua University, 100084, Beijing, China;

  • c. Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117576, Singapore

Funds:

D Program of China (No. 2022ZD0117501), the Singapore RIE2020 Advanced Manufacturing and Engineering Programmatic Grant by the Agency for Science, Technology and Research (A∗STAR) under grant no. A1898b0043, Tsinghua University Initiative Scientific Research Program and Low Carbon Energy Research Funding Initiative by A∗STAR under grant number A-8000182-00-00.

This work is supported by the National Key R&

  • Received date 31 August 2022, Accepted date 01 December 2022, RevRecd date 16 November 2022, Publish date 07 December 2022,
    Abstract
  • Membrane technologies are becoming increasingly versatile and helpful today for sustainable development. Machine Learning (ML), an essential branch of artificial intelligence (AI), has substantially impacted the research and development norm of new materials for energy and environment. This review provides an overview and perspectives on ML methodologies and their applications in membrane design and discovery. A brief overview of membrane technologies is first provided with the current bottlenecks and potential solutions. Through an applications-based perspective of AI-aided membrane design and discovery, we further show how ML strategies are applied to the membrane discovery cycle (including membrane material design, membrane application, membrane process design, and knowledge extraction), in various membrane systems, ranging from gas, liquid, and fuel cell separation membranes. Furthermore, the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed. The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end.

     

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    • References(134)
  • [1]
    F.M. Dautzenberg, M. Mukherjee, Chem. Eng. Sci. 56 (2001) 251-267.
    [2]
    P. Bernardo, E. Drioli, G. Golemme, Ind. Eng. Chem. Res. 48 (2009) 4638-4663.
    [3]
    V.D. Thuc, V.D. Cong Tinh, D. Kim, J. Membr. Sci. 642 (2022) 119990.
    [4]
    A. Yadav, P.K. Labhasetwar, V.K. Shahi, Sep. Purif. Technol. 271 (2021) 118840.
    [5]
    M. Soto-Herranz, M. Sanchez-Bascones, J.M. Antolin-Rodriguez, M.B. Vanotti, P. Martin-Ramos, Membranes 11 (2021).
    [6]
    T. Zhang, R.-Y. Fu, K.-P. Wang, Y.-W. Gao, H.-R. Li, X.-M. Wang, Y.F. Xie, L.A. Hou, J. Membr. Sci. 647 (2022) 120304.
    [7]
    A. Bharti, S. Muliankeezhu, G. Cheruvally, J. Nanosci. Nanotechnol. 18 (2018) 2781-2789.
    [8]
    R. Singh, D. Kim, Nano Energy 92 (2022) 106690.
    [9]
    M.R. Abukhadra, M.H. Eid, A.M. El-Sherbeeny, A.E.E. Abd Elgawad, J.-J. Shim, J. Contam. Hydrol. 244 (2022) 103923.
    [10]
    S. Bai, B. Kim, C. Kim, O. Tamwattana, H. Park, J. Kim, D. Lee, K. Kang, Nat. Nanotechnol. 16 (2021) 77-84.
    [11]
    W. Li, Y. Li, J. Caro, A. Huang, J. Membr. Sci. 643 (2022) 120021.
    [12]
    Z. Dai, L. Ansaloni, L. Deng, Green Energy Environ. 1 (2016) 102-128.
    [13]
    D.-H. Lee, G.-T. Yun, G. Doo, S. Yuk, H. Guim, Y. Kim, W.-B. Jung, H.-T. Jung, H.-T. Kim, Nano Lett. 22 (2022) 1174-1182.
    [14]
    R. Darvishi, E. Pakizeh, Int. J. Polym. Sci. 2020 (2020) 1018347.
    [15]
    R.M. Venable, A. Kramer, R.W. Pastor, Chem. Rev. 119 (2019) 5954-5997.
    [16]
    K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh, Nature 559 (2018) 547-555.
    [17]
    G.R. Berdiyorov, M.E. Madjet, K.A. Mahmoud, Membranes 11 (2021).
    [18]
    T. Li, W. Lu, Z. Yuan, H. Zhang, X. Li, J. Mater. Chem. A 9 (2021) 14545-14552.
    [19]
    T.M. Mitchell, The discipline of machine learning, Carnegie Mellon University, School of Computer Science, Machine Learning Dept., Pittsburgh, 2006.
    [20]
    M. Takht Ravanchi, T. Kaghazchi, A. Kargari, Desalination 235 (2009) 199-244.
    [21]
    A.G. Fane, R. Wang, Y. Jia, in Membrane and Desalination Technologies, eds. Wang, L. K.; Chen, J. P.; Hung, Y.-T.; Shammas, N. K., Humana Press, Totowa, NJ, 2011, pp. 1-45.
    [22]
    A. Sagle, B. Freeman, Fundamentals of membranes for water treatment. In The future of desalination in Texas, Texas Water Development Board Austin, TX: 2004; Vol. 2, p 137.
    [23]
    R.W. Baker, Membrane technology and applications, John Wiley & Sons, New York, 2012.
    [24]
    R. Mallada, M. Menendez, Inorganic membranes: synthesis, characterization and applications, Elsevier, Burlington, 2008.
    [25]
    U.M. Aliyu, S. Rathilal, Y.M. Isa, Water Pract. Technol. 13 (2018) 738-752.
    [26]
    S. Zhang, L. Shen, H. Deng, Q. Liu, X. You, J. Yuan, Z. Jiang, S. Zhang, Adv. Mater. 34 (2022) 2108457.
    [27]
    J. Dechnik, J. Gascon, C.J. Doonan, C. Janiak, C.J. Sumby, Angew. Chem. Int. Ed. 56 (2017) 9292-9310.
    [28]
    H. Siddique, E. Rundquist, Y. Bhole, L.G. Peeva, A.G. Livingston, J. Membr. Sci. 452 (2014) 354-366.
    [29]
    M. Vinoba, M. Bhagiyalakshmi, Y. Alqaheem, A.A. Alomair, A. Perez, M.S. Rana, Sep. Purif. Technol. 188 (2017) 431-450.
    [30]
    M. Galizia, W.S. Chi, Z.P. Smith, T.C. Merkel, R.W. Baker, B.D. Freeman, Macromolecules 50 (2017) 7809-7843.
    [31]
    M. Mulder, J. Mulder, Basic principles of membrane technology, Springer Netherlands, Dordrecht, 1996.
    [32]
    S.K. Patel, C.L. Ritt, A. Deshmukh, Z. Wang, M. Qin, R. Epsztein, M. Elimelech, Energy Environ. Sci. 13 (2020) 1694-1710.
    [33]
    D. Cohen-Tanugi, R.K. Mcgovern, S.H. Dave, J.H. Lienhard, J.C. Grossman, Energy Environ. Sci. 7 (2014) 1134-1141.
    [34]
    F. Li, C. Fang, W. Liu, L. Yang, B. Guo, S. Zhang, J. Membr. Sci. 631 (2021) 119317.
    [35]
    Z. Yang, H. Guo, C.Y. Tang, J. Membr. Sci. 590 (2019) 117297.
    [36]
    Y. Zhang, C. Wang, C. Wang, Y. Zhang, J. Zhao, N. Wei, Green Energy Environ. (2022).
    [37]
    X. Liu, Y. Shan, S. Zhang, Q. Kong, H. Pang, Green Energy Environ. (2022).
    [38]
    C.Y. Chuah, K. Goh, T.-H. Bae, Membranes 11 (2021).
    [39]
    Y. Liu, Z. Liu, B.E. Kraftschik, V.P. Babu, N. Bhuwania, D. Chinn, W.J. Koros, J. Membr. Sci. 632 (2021) 119361.
    [40]
    G. Bang, D.-K. Moon, J.-H. Kang, Y.-J. Han, K.-M. Kim, C.-H. Lee, Chem. Eng. J. 411 (2021) 128473.
    [41]
    Y. Zhou, Y. Zhang, J. Xue, R. Wang, Z. Yin, L. Ding, H. Wang, Chem. Eng. J. 420 (2021) 129574.
    [42]
    A. Gantenbein, J. Witte, S.M.A. Biollaz, O. Krocher, T.J. Schildhauer, Chem. Eng. Sci. 229 (2021) 116012.
    [43]
    A. Roy, R. Venna Surendar, G. Rogers, L. Tang, C. Fitzgibbons Thomas, J. Liu, H. Mccurry, J. Vickery David, D. Flick, B. Fish, Proc. Natl. Acad. Sci. 118 (2021) e2022194118.
    [44]
    C.Z. Liang, T.-S. Chung, J.-Y. Lai, Prog. Polym. Sci. 97 (2019) 101141.
    [45]
    J. Caro, M. Noack, P. Kolsch, R. Schafer, Microporous Mesoporous Mater. 38 (2000) 3-24.
    [46]
    W.-S. Sun, M.-J. Yin, W.-H. Zhang, S. Li, N. Wang, Q.-F. An, Green Energy Environ. (2022).
    [47]
    L.M. Robeson, J. Membr. Sci. 62 (1991) 165-185.
    [48]
    L.M. Robeson, J. Membr. Sci. 320 (2008) 390-400.
    [49]
    A.F. Ismail, W. Lorna, Sep. Purif. Technol. 27 (2002) 173-194.
    [50]
    T.C. Merkel, H. Lin, X. Wei, R. Baker, J. Membr. Sci. 359 (2010) 126-139.
    [51]
    T. Scarazzato, Z. Panossian, J.a.S. Tenorio, V. Perez-Herranz, D.C.R. Espinosa, Desalination 436 (2018) 114-124.
    [52]
    S.J. Peighambardoust, S. Rowshanzamir, M. Amjadi, Int. J. Hydrog. Energy 35 (2010) 9349-9384.
    [53]
    J. Wu, X.Z. Yuan, J.J. Martin, H. Wang, J. Zhang, J. Shen, S. Wu, W. Merida, J. Power Sources 184 (2008) 104-119.
    [54]
    J. Li, K. Lim, H. Yang, Z. Ren, S. Raghavan, P.-Y. Chen, T. Buonassisi, X. Wang, Matter 3 (2020) 393-432.
    [55]
    Z. Zhou, S. Kearnes, L. Li, R.N. Zare, P. Riley, Sci. Rep. 9 (2019) 10752.
    [56]
    R.K. Kaliyar, A. Goswami, P. Narang, S. Sinha, Cogn. Syst. Res. 61 (2020) 32-44.
    [57]
    P. Satjaritanun, M. O'brien, D. Kulkarni, S. Shimpalee, C. Capuano, K.E. Ayers, N. Danilovic, D.Y. Parkinson, I.V. Zenyuk, iScience 23 (2020) 101783.
    [58]
    M.E. Gunay, N.A. Tapan, G. Akkoc, Int. J. Hydrog. Energy 47 (2022) 2134-2151.
    [59]
    G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning, Springer, 2013.
    [60]
    L. Breiman, Mach. Learn. 24 (1996) 123-140.
    [61]
    L. Breiman, Mach. Learn. 45 (2001) 5-32.
    [62]
    Z. Zhang, Y. Luo, H. Peng, Y. Chen, R.-Z. Liao, Q. Zhao, J. Membr. Sci. 620 (2021) 118910.
    [63]
    J.H. Friedman, Comput. Stat. Data. Anal. 38 (2002) 367-378.
    [64]
    T. Chen, C. Guestrin, XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery: San Francisco, California, USA, 2016; pp 785-794.
    [65]
    A.V. Dorogush, V. Ershov, A. Gulin, arXiv preprint arXiv:1810.11363 (2018).
    [66]
    O. Bousquet, U. Von Luxburg, G. Ratsch, Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tubingen, Germany, August 4-16, 2003, Revised Lectures, Springer, 2011.
    [67]
    N. Hvala, J. Kocijan, Comput. Chem. Eng. 140 (2020) 106934.
    [68]
    L. Zhu, J. Chen, Energy 149 (2018) 63-73.
    [69]
    H. Adib, A. Raisi, B. Salari, Res. Chem. Intermed. 45 (2019) 5725-5743.
    [70]
    X. Meng, F. Wang, S. Meng, R. Wang, Z. Mao, Y. Li, M. Yu, X. Wang, Q. Zhao, L. Yang, Membranes 11 (2021).
    [71]
    W.S. Noble, Nat. Biotechnol. 24 (2006) 1565-1567.
    [72]
    M. Awad, R. Khanna, in Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, eds. Awad, M.; Khanna, R., Apress, Berkeley, CA, 2015, pp. 67-80.
    [73]
    J. Liu, X. Kang, X. Luan, L. Gao, H. Tian, X. Liu, Environ. Technol. Innov. 19 (2020) 100844.
    [74]
    A.K. Jain, M. Jianchang, K.M. Mohiuddin, Computer 29 (1996) 31-44.
    [75]
    S.I. Gallant, IEEE Trans. Neural. Netw. 1 (1990) 179-191.
    [76]
    L. Noriega, Multilayer Perceptron Tutorial, School of Computing, Staffordshire University, 2005.
    [77]
    Y. Lecun, Y. Bengio, G. Hinton, Nature 521 (2015) 436-444.
    [78]
    W. Rawat, Z. Wang, Neural. Comput. 29 (2017) 2352-2449.
    [79]
    S. Zhou, Y. Lu, D. Bao, K. Wang, J. Shan, Z. Hou, Int. J. Hydrog. Energy 47 (2022) 10976-10989.
    [80]
    Z.C. Lipton, J. Berkowitz, C. Elkan, arXiv preprint arXiv:1506.00019 (2015).
    [81]
    J. Zuo, H. Lv, D. Zhou, Q. Xue, L. Jin, W. Zhou, D. Yang, C. Zhang, Appl. Energy 281 (2021) 115937.
    [82]
    G. Ignacz, G. Szekely, J. Membr. Sci. 646 (2022) 120268.
    [83]
    B. Teychene, A. Touffet, J. Baron, B. Welte, M. Joyeux, H. Gallard, Water Res. 129 (2018) 365-374.
    [84]
    Z. Liu, M. Pei, Q. He, Q. Wu, L. Jackson, L. Mao, J. Power Sources 482 (2021) 228894.
    [85]
    X. Sun, Y. Zhou, L. Huang, J. Lian, Int. J. Hydrog. Energy 46 (2021) 39983-40000.
    [86]
    I.T. Jolliffe, J. Cadima, Philos. Trans. Royal Soc. A 374 (2016) 20150202.
    [87]
    M.-K. Kim, J.W. Chang, K. Park, D.R. Yang, J. Membr. Sci. 641 (2022) 119909.
    [88]
    P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, Association for Computing Machinery: Helsinki, Finland, 2008; pp 1096-1103.
    [89]
    L. Van Der Maaten, G. Hinton, J. Mach. Learn. Res. 9 (2008).
    [90]
    X. Zhang, X. Guo, Int. J. Hydrog. Energy 46 (2021) 30828-30840.
    [91]
    B. Settles, Active learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, 2009.
    [92]
    N. Wilson, D. Willhelm, X. Qian, R. Arroyave, X. Qian, Comput. Mater. Sci. 208 (2022) 111330.
    [93]
    S. Valle, W. Li, S.J. Qin, Ind. Eng. Chem. Res. 38 (1999) 4389-4401.
    [94]
    S. Curtarolo, W. Setyawan, G.L.W. Hart, M. Jahnatek, R.V. Chepulskii, R.H. Taylor, S. Wang, J. Xue, K. Yang, O. Levy, M.J. Mehl, H.T. Stokes, D.O. Demchenko, D. Morgan, Comput. Mater. Sci. 58 (2012) 218-226.
    [95]
    A.K. Malde, L. Zuo, M. Breeze, M. Stroet, D. Poger, P.C. Nair, C. Oostenbrink, A.E. Mark, Journal of Chemical Theory and Computation 7 (2011) 4026-4037.
    [96]
    Y. Xu, M. Yamazaki, P. Villars, Jpn. J. Appl. Phys. 50 (2011) 11RH02.
    [97]
    C.R. Groom, I.J. Bruno, M.P. Lightfoot, S.C. Ward, Acta Crystallographica Section B 72 (2016) 171-179.
    [98]
    Y. Zhao, M. Al-Fahdi, M. Hu, E.M.D. Siriwardane, Y. Song, A. Nasiri, J. Hu, Advanced Science 8 (2021) 2100566.
    [99]
    Catalysis Hub, https://www.catalysis-hub.org/.
    [100]
    R. Johnson, Computational Chemistry Comparison and Benchmark Database 2018.
    [101]
    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, Journal of Chemical & Engineering Data 64 (2019) 5985-5998.
    [102]
    S. Haastrup, M. Strange, M. Pandey, T. Deilmann, P.S. Schmidt, N.F. Hinsche, M.N. Gjerding, D. Torelli, P.M. Larsen, A.C. Riis-Jensen, 2D Materials 5 (2018) 042002.
    [103]
    A. Vaitkus, A. Merkys, S. Grazulis, J. Appl. Crystallogr. 54 (2021) 661-672.
    [104]
    HybriD3 Materials Database, https://materials.hybrid3.duke.edu/.
    [105]
    D. Zagorac, H. Muller, S. Ruehl, J. Zagorac, S. Rehme, J. Appl. Crystallogr. 52 (2019) 918-925.
    [106]
    N.N. Kiselyova, V.A. Dudarev, A.V. Stolyarenko, High Temperature 54 (2016) 215-222.
    [107]
    Material Project, https://materialsproject.org/.
    [108]
    S.S. Borysov, R.M. Geilhufe, A.V. Balatsky, PLOS ONE 12 (2017) e0171501.
    [109]
    R. Tran, J. Lan, M. Shuaibi, S. Goyal, B.M. Wood, A. Das, J. Heras-Domingo, A. Kolluru, A. Rizvi, N. Shoghi, arXiv preprint arXiv:2206.08917 (2022).
    [110]
    S. Kirklin, J.E. Saal, B. Meredig, A. Thompson, J.W. Doak, M. Aykol, S. Ruhl, C. Wolverton, npj Computational Materials 1 (2015) 15010.
    [111]
    S. Otsuka, I. Kuwajima, J. Hosoya, Y. Xu, M. Yamazaki, 2011 International Conference on Emerging Intelligent Data and Web Technologies, 2011, p 22-29.
    [112]
    Polymer Gas Separation Membrane Database, https://membrane-australasia.org/.
    [113]
    C. Kim, A. Chandrasekaran, T.D. Huan, D. Das, R. Ramprasad, The Journal of Physical Chemistry C 122 (2018) 17575-17585.
    [114]
    J.W. Barnett, R. Bilchak Connor, Y. Wang, C. Benicewicz Brian, A. Murdock Laura, T. Bereau, K. Kumar Sanat, Sci. Adv. 6 (2020) eaaz4301.
    [115]
    J. Guan, T. Huang, W. Liu, F. Feng, S. Japip, J. Li, X. Wang, S. Zhang, Cell Rep. Phys. Sci. 3 (2022) 100864.
    [116]
    D. Jha, K. Choudhary, F. Tavazza, W.-K. Liao, A. Choudhary, C. Campbell, A. Agrawal, Nat. Commun. 10 (2019) 5316.
    [117]
    Q. Yuan, M. Longo, A.W. Thornton, N.B. Mckeown, B. Comesana-Gandara, J.C. Jansen, K.E. Jelfs, J. Membr. Sci. 627 (2021) 119207.
    [118]
    S. Van Buuren, K. Groothuis-Oudshoorn, J. Stat. Softw. 45 (2011) 1 - 67.
    [119]
    D.J.C. Mackay, Neural. Comput. 4 (1992) 415-447.
    [120]
    P. Geurts, D. Ernst, L. Wehenkel, Mach. Learn. 63 (2006) 3-42.
    [121]
    Z. Zhang, X. Cao, C. Geng, Y. Sun, Y. He, Z. Qiao, C. Zhong, J. Membr. Sci. 650 (2022) 120399.
    [122]
    H. Daglar, S. Keskin, ACS Applied Materials & Interfaces 14 (2022) 32134-32148.
    [123]
    R.S. Olson, R.J. Urbanowicz, P.C. Andrews, N.A. Lavender, L.C. Kidd, J.H. Moore, Applications of Evolutionary Computation, Cham, 2016, p 123-137.
    [124]
    D. Rall, D. Menne, A.M. Schweidtmann, J. Kamp, L. Von Kolzenberg, A. Mitsos, M. Wessling, J. Membr. Sci. 569 (2019) 209-219.
    [125]
    L. Ritt Cody, M. Liu, A. Pham Tuan, R. Epsztein, J. Kulik Heather, M. Elimelech, Sci. Adv. 8 (2022) eabl5771.
    [126]
    H. Gao, S. Zhong, W. Zhang, T. Igou, E. Berger, E. Reid, Y. Zhao, D. Lambeth, L. Gan, M.A. Afolabi, Z. Tong, G. Lan, Y. Chen, Environ. Sci. Technol. 56 (2022) 2572-2581.
    [127]
    W.R. Bowen, J.S. Welfoot, Chem. Eng. Sci. 57 (2002) 1121-1137.
    [128]
    M. Fetanat, M. Keshtiara, R. Keyikoglu, A. Khataee, R. Daiyan, A. Razmjou, Sep. Purif. Technol. 270 (2021) 118383.
    [129]
    D. Rall, A.M. Schweidtmann, B.M. Aumeier, J. Kamp, J. Karwe, K. Ostendorf, A. Mitsos, M. Wessling, J. Membr. Sci. 600 (2020) 117860.
    [130]
    D. Rall, A.M. Schweidtmann, M. Kruse, E. Evdochenko, A. Mitsos, M. Wessling, J. Membr. Sci. 608 (2020) 118208.
    [131]
    C. Odabasi, P. Dologlu, F. Gulmez, G. Kusoglu, O. Caglar, Comput. Chem. Eng. 159 (2022) 107669.
    [132]
    T.M. Razakh, B. Wang, S. Jackson, R.K. Kalia, A. Nakano, K.-I. Nomura, P. Vashishta, SoftwareX 15 (2021) 100789.
    [133]
    M. Mirza, S. Osindero, ArXiv abs/1411.1784 (2014).
    [134]
    J.-Y. Zhu, T. Park, P. Isola, A.A. Efros, arXiv preprint arXiv:1703.10593 (2017).
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