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Computational development of the nanoporous materials genome

Abstract

There is currently a push towards big data and data mining in materials research to accelerate discovery. Zeolites, metal–organic frameworks and other related crystalline porous materials are not immune to this phenomenon, as evidenced by the proliferation of porous structure databases and computational gas-adsorption screening studies over the past decade. The endeavour to identify the best materials for various gas separation and storage applications has led not only to thousands of synthesized structures, but also to the development of algorithms for building hypothetical materials. The materials databases assembled with these algorithms contain a much wider range of complex pore structures than have been synthesized, with the reasoning being that we have discovered only a small fraction of realizable structures and expanding upon these will accelerate rational design. In this Review, we highlight the methods developed to build these databases, and some of the important outcomes from large-scale computational screening studies.

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Figure 1: Property distributions of MOF, zeolite, PPN and ZIF databases in the nanoporous materials genome.
Figure 2: The building of the prototypical MOF, HKUST-1, using different assembly methods.
Figure 3: Challenges associated with assembling new MOFs with the Tinkertoy approach.
Figure 4: The challenge of devising frameworks to attain target values for methane storage.
Figure 5: Examples of correlations between physical characteristics of pores and performance.
Figure 6: Development and usages of a descriptor using a topological data analysis technique.

References

  1. 1

    Flanigen, E. M., Broach, R. W. & Wilson, S. T. in Zeolites in Industrial Separation and Catalysis 1–26 (Wiley, 2010).

    Book  Google Scholar 

  2. 2

    Zimmermann, N. E. R. & Haranczyk, M. History and utility of zeolite framework-type discovery from a data-science perspective. Cryst. Growth Des. 16, 3043–3048 (2016).

    CAS  Article  Google Scholar 

  3. 3

    Hoskin, B. F. & Robson, R. Design and construction of a new class of scaffolding-like materials comprising infinite polymeric frameworks of 3D-linked molecular rods. A reappraisal of the Zn(CN)2 and Cd(CN)2 structures and the synthesis and structure of the diamond-related frameworks [N(CH3)4][CuIZnII(CN)4] and CuI[4,4′,4″,4‴-tetracyanotetraphenylmethane]BF4∙x C6H5NO2 . J. Am. Chem. Soc. 112, 1546–1554 (1990).

    Article  Google Scholar 

  4. 4

    Chui, S. S.-Y., Lo, S. M.-F., Charmant, J. P. H., Orpen, A. G. & Williams, I. D. A. Chemically functionalizable nanoporous material [Cu3(TMA)2(H2O)3]n . Science 283, 1148–1150 (1999).

    CAS  Article  Google Scholar 

  5. 5

    Li, H., Eddaoudi, M., O’Keeffe, M. & Yaghi, O. M. Design and synthesis of an exceptionally stable and highly porous metal–organic framework. Nature 402, 276–279 (1999).

    CAS  Article  Google Scholar 

  6. 6

    Yaghi, O. M. & Li, H. Hydrothermal synthesis of a metal–organic framework containing large rectangular channels. J. Am. Chem. Soc. 117, 10401–10402 (1995).

    CAS  Article  Google Scholar 

  7. 7

    Batten, S. R. et al. Terminology of metal–organic frameworks and coordination polymers (IUPAC Recommendations 2013). Pure Appl. Chem. 85, 1715–1724 (2013).

    CAS  Article  Google Scholar 

  8. 8

    Côté, A. P. et al. Porous, crystalline, covalent organic frameworks. Science 310, 1166–1170 (2005).

    Article  CAS  Google Scholar 

  9. 9

    El-Kaderi, H. M. et al. Designed synthesis of 3D covalent organic frameworks. Science 316, 268–272 (2007).

    CAS  Article  Google Scholar 

  10. 10

    Banerjee, R. et al. High-throughput synthesis of zeolitic imidazolate frameworks and application to CO2 capture. Science 319, 939–943 (2008).

    CAS  Article  Google Scholar 

  11. 11

    Lu, W. et al. Porous polymer networks: synthesis, porosity, and applications in gas storage/separation. Chem. Mater. 22, 5964–5972 (2010).

    CAS  Article  Google Scholar 

  12. 12

    Yaghi, O. M. et al. Reticular synthesis and the design of new materials. Nature 423, 705–714 (2003).

    CAS  Article  Google Scholar 

  13. 13

    Ockwig, N. W., Delgado-Friedrichs, O., O’Keeffe, M. & Yaghi, O. M. Reticular chemistry: occurrence and taxonomy of nets and grammar for the design of frameworks. Acc. Chem. Res. 38, 176–182 (2005).

    CAS  Article  Google Scholar 

  14. 14

    Delgado-Friedrichs, O., O’Keeffe, M. & Yaghi, O. M. Taxonomy of periodic nets and the design of materials. Phys. Chem. Chem. Phys. 9, 1035–1043 (2007).

    CAS  Article  Google Scholar 

  15. 15

    Bonneau, C., Delgado-Friedrichs, O., O’Keeffe, M. & Yaghi, O. M. Three-periodic nets and tilings: minimal nets. Acta Crystallogr. A. 60, 517–520 (2004).

    Article  CAS  Google Scholar 

  16. 16

    Delgado-Friedrichs, O. & O’Keeffe, M. Crystal nets as graphs: terminology and definitions. J. Solid State Chem. 178, 2480–2485 (2005).

    CAS  Article  Google Scholar 

  17. 17

    Li, M., Li, D., O’Keeffe, M. & Yaghi, O. M. Topological analysis of metal–organic frameworks with polytopic linkers and/or multiple building units and the minimal transitivity principle. Chem. Rev. 114, 1343–1370 (2014).

    CAS  Article  Google Scholar 

  18. 18

    O’Keeffe, M. & Yaghi, O. M. Deconstructing the crystal structures of metal–organic frameworks and related materials into their underlying nets. Chem. Rev. 112, 675–702 (2012).

    Article  CAS  Google Scholar 

  19. 19

    Schoedel, A., Li, M., Li, D., O’Keeffe, M. & Yaghi, O. M. Structures of metal–organic frameworks with rod secondary building units. Chem. Rev. 116, 12466–12535 (2016).

    CAS  Article  Google Scholar 

  20. 20

    Blatov, V. A., Carlucci, L., Ciani, G. & Proserpio, D. M. Interpenetrating metal–organic and inorganic 3D networks: a computer-aided systematic investigation. Part I. Analysis of the Cambridge Structural Database. CrystEngComm 6, 377–395 (2004).

    CAS  Article  Google Scholar 

  21. 21

    Alexandrov, E. V., Blatov, V. A., Kochetkov, A. V. & Proserpio, D. M. Underlying nets in three-periodic coordination polymers: topology, taxonomy and prediction from a computer-aided analysis of the Cambridge Structural Database. CrystEngComm 13, 3947–3958 (2011).

    CAS  Article  Google Scholar 

  22. 22

    Farha, O. K. et al. De novo synthesis of a metal–organic framework material featuring ultrahigh surface area and gas storage capacities. Nat. Chem. 2, 944–948 (2010).

    CAS  Article  Google Scholar 

  23. 23

    Vaidhyanathan, R. et al. Direct observation and quantification of CO2 binding within an amine-functionalized nanoporous solid. Science 330, 650–653 (2010).

    CAS  Article  Google Scholar 

  24. 24

    McDonald, T. M. et al. Cooperative insertion of CO2 in diamine-appended metal–organic frameworks. Nature 519, 303–308 (2015).

    CAS  Article  Google Scholar 

  25. 25

    Lyne, P. D. Structure-based virtual screening: an overview. Drug Discov. Today 7, 1047–1055 (2002).

    CAS  Article  Google Scholar 

  26. 26

    Colón, Y. J. & Snurr, R. Q. High-throughput computational screening of metal–organic frameworks. Chem. Soc. Rev. 43, 5735–5749 (2014).

    Article  Google Scholar 

  27. 27

    Martin, R. L. et al. In silico design of three-dimensional porous covalent organic frameworks via known synthesis routes and commercially available species. J. Phys. Chem. C 118, 23790–23802 (2014).

    CAS  Article  Google Scholar 

  28. 28

    Simon, C. M. et al. Optimizing nanoporous materials for gas storage. Phys. Chem. Chem. Phys. 16, 5499–5513 (2014).

    CAS  Article  Google Scholar 

  29. 29

    Simon, C. M. et al. The materials genome in action: identifying the performance limits for methane storage. Energy Environ. Sci. 8, 1190–1199 (2015).

    CAS  Article  Google Scholar 

  30. 30

    Martin, R. L., Simon, C. M., Smit, B. & Haranczyk, M. In silico design of porous polymer networks: high-throughput screening for methane storage materials. J. Am. Chem. Soc. 136, 5006–5022 (2014).

    CAS  Article  Google Scholar 

  31. 31

    Ohno, H. & Mukae, Y. Machine learning approach for prediction and search: application to methane storage in a metal–organic framework. J. Phys. Chem. C 120, 23963–23968 (2016).

    CAS  Article  Google Scholar 

  32. 32

    Martin, R. L., Lin, L.-C., Jariwala, K., Smit, B. & Haranczyk, M. Mail-order metal–organic frameworks (MOFs): designing isoreticular MOF-5 analogues comprising commercially available organic molecules. J. Phys. Chem. C 117, 12159–12167 (2013).

    CAS  Article  Google Scholar 

  33. 33

    Kim, J. et al. New materials for methane capture from dilute and medium-concentration sources. Nat. Commun. 4, 1694 (2013).

    Article  CAS  Google Scholar 

  34. 34

    Gómez-Gualdrón, D. A. et al. Impact of the strength and spatial distribution of adsorption sites on methane deliverable capacity in nanoporous materials. Chem. Eng. Sci. 159, 18–30 (2017).

    Article  CAS  Google Scholar 

  35. 35

    Gómez-Gualdrón, D. A., Wilmer, C. E., Farha, O. K., Hupp, J. T. & Snurr, R. Q. Exploring the limits of methane storage and delivery in nanoporous materials. J. Phys. Chem. C 118, 6941–6951 (2014).

    Article  CAS  Google Scholar 

  36. 36

    Fu, J., Tian, Y. & Wu, J. Seeking metal–organic frameworks for methane storage in natural gas vehicles. Adsorption 21, 499–507 (2015).

    CAS  Article  Google Scholar 

  37. 37

    Keskin, S. & Sholl, D. S. Efficient methods for screening of metal organic framework membranes for gas separations using atomically detailed models. Langmuir 25, 11786–11795 (2009).

    CAS  Article  Google Scholar 

  38. 38

    Wu, D. et al. Large-scale computational screening of metal–organic frameworks for CH4/H2 separation. AIChE J. 58, 2078–2084 (2012).

    CAS  Article  Google Scholar 

  39. 39

    Yazaydın, A. O. et al. Screening of metal–organic frameworks for carbon dioxide capture from flue gas using a combined experimental and modeling approach. J. Am. Chem. Soc. 131, 18198–18199 (2009).

    Article  CAS  Google Scholar 

  40. 40

    Koh, H. S., Rana, M. K., Hwang, J. & Siegel, D. J. Thermodynamic screening of metal-substituted MOFs for carbon capture. Phys. Chem. Chem. Phys. 15, 4573 (2013).

    CAS  Article  Google Scholar 

  41. 41

    Krishna, R. & van Baten, J. M. In silico screening of metal–organic frameworks in separation applications. Phys. Chem. Chem. Phys. 13, 10593–10616 (2011).

    CAS  Article  Google Scholar 

  42. 42

    Chung, Y. G. et al. Computation-ready, experimental metal–organic frameworks: a tool to enable high-throughput screening of nanoporous crystals. Chem. Mater. 26, 6185–6192 (2014).

    CAS  Article  Google Scholar 

  43. 43

    Allen, F. H. The Cambridge Structural Database: a quarter of a million crystal structures and rising. Acta Crystallogr. Sect. B Struct. Sci. 58, 380–388 (2002).

    Article  CAS  Google Scholar 

  44. 44

    Pophale, R., Cheeseman, P. A. & Deem, M. W. A database of new zeolite-like materials. Phys. Chem. Chem. Phys. 13, 12407–12412 (2011).

    CAS  Article  Google Scholar 

  45. 45

    Deem, M. W., Pophale, R., Cheeseman, P. A. & Earl, D. J. Computational discovery of new zeolite-like materials. J. Phys. Chem. C 113, 21353–21360 (2009).

    CAS  Article  Google Scholar 

  46. 46

    Lin, L.-C. et al. In silico screening of carbon-capture materials. Nat. Mater. 11, 633–641 (2012).

    CAS  Article  Google Scholar 

  47. 47

    Kim, J., Lin, L., Swisher, J. A., Haranczyk, M. & Smit, B. Predicting large CO2 adsorption in aluminosilicate zeolites for postcombustion carbon dioxide capture. J. Am. Chem. Soc. 134, 18940–18943 (2012).

    CAS  Article  Google Scholar 

  48. 48

    Kim, J., Abouelnasr, M., Lin, L.-C. & Smit, B. Large-scale screening of zeolite structures for CO2 membrane separations. J. Am. Chem. Soc. 135, 7545–7552 (2013).

    CAS  Article  Google Scholar 

  49. 49

    Kim, J. et al. Large-scale computational screening of zeolites for ethane/ethene separation. Langmuir 28, 11914–11919 (2012).

    CAS  Article  Google Scholar 

  50. 50

    Bai, P. et al. Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling. Nat. Commun. 6, 5912 (2015).

    CAS  Article  Google Scholar 

  51. 51

    Mellot Draznieks, C., Newsam, J. M., Gorman, A. M., Freeman, C. M. & Férey, G. De novo prediction of inorganic structures developed through Automated Assembly of Secondary Building Units (AASBU method). Angew. Chem. Int. Ed. 39, 2270–2275 (2000).

    CAS  Article  Google Scholar 

  52. 52

    Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671–680 (1983).

    CAS  Article  Google Scholar 

  53. 53

    Falcioni, M. & Deem, M. W. A biased Monte Carlo scheme for zeolite structure solution. J. Chem. Phys. 110, 1754–1766 (1999).

    CAS  Article  Google Scholar 

  54. 54

    Deem, M. W. & Newsam, J. M. Determination of 4-connected framework crystal structures by simulated annealing. Nature 342, 260–262 (1989).

    CAS  Article  Google Scholar 

  55. 55

    Mellot-Draznieks, C., Dutour, J. & Férey, G. Hybrid organic–inorganic frameworks: routes for computational design and structure prediction. Angew. Chem. Int. Ed. 43, 6290–6296 (2004).

    CAS  Article  Google Scholar 

  56. 56

    Mellot-Draznieks, C. et al. Computational design and prediction of interesting not-yet-synthesized structures of inorganic materials by using building unit concepts. Chem. Eur. J. 8, 4102–4113 (2002).

    CAS  Article  Google Scholar 

  57. 57

    Wilmer, C. E. et al. Large-scale screening of hypothetical metal–organic frameworks. Nat. Chem. 4, 83–89 (2012).

    CAS  Article  Google Scholar 

  58. 58

    Chen, B., Eddaoudi, M., Hyde, S. T., O’Keeffe, M. & Yaghi, O. M. Interwoven metal–organic framework on a periodic minimal surface with extra-large pores. Science 291, 1021–1023 (2001).

    CAS  Article  Google Scholar 

  59. 59

    Skiena, S. S. in The Algorithm Design Manual Ch. 2 31–65 (Springer, 2009).

    Google Scholar 

  60. 60

    Sikora, B. J., Winnegar, R., Proserpio, D. M. & Snurr, R. Q. Textural properties of a large collection of computationally constructed MOFs and zeolites. Micropor. Mesopor. Mater. 186, 207–213 (2014).

    CAS  Article  Google Scholar 

  61. 61

    Martin, R. L. & Haranczyk, M. Exploring frontiers of high surface area metal–organic frameworks. Chem. Sci. 4, 1781–1785 (2013).

    CAS  Article  Google Scholar 

  62. 62

    Martin, R. L. & Haranczyk, M. Optimization-based design of metal–organic framework materials. J. Chem. Theory Comput. 9, 2816–2825 (2013).

    CAS  Article  Google Scholar 

  63. 63

    Bao, Y., Martin, R. L., Haranczyk, M. & Deem, M. W. In silico prediction of MOFs with high deliverable capacity or internal surface area. Phys. Chem. Chem. Phys. 17, 11962–11973 (2015).

    CAS  Article  Google Scholar 

  64. 64

    Gómez-Gualdrón, D. A. et al. Evaluating topologically diverse metal–organic frameworks for cryo-adsorbed hydrogen storage. Energy Environ. Sci. 9, 3279–3289 (2016).

    Article  CAS  Google Scholar 

  65. 65

    Wang, T. C. et al. Ultrahigh surface area zirconium MOFs and insights into the applicability of the BET theory. J. Am. Chem. Soc. 137, 3585–3591 (2015).

    CAS  Article  Google Scholar 

  66. 66

    Coudert, F.-X. & Fuchs, A. H. Computational characterization and prediction of metal–organic framework properties. Coord. Chem. Rev. 307, 211–236 (2016).

    CAS  Article  Google Scholar 

  67. 67

    Martin, R. L. & Haranczyk, M. Construction and characterization of structure models of crystalline porous polymers. Cryst. Growth Des. 14, 2431–2440 (2014).

    CAS  Article  Google Scholar 

  68. 68

    Willems, T. F., Rycroft, C. H., Kazi, M., Meza, J. C. & Haranczyk, M. Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials. Micropor. Mesopor. Mater. 149, 134–141 (2012).

    CAS  Article  Google Scholar 

  69. 69

    Pinheiro, M. et al. Characterization and comparison of pore landscapes in crystalline porous materials. J. Mol. Graph. Model. 44, 208–219 (2013).

    CAS  Article  Google Scholar 

  70. 70

    O’Keeffe, M., Peskov, M. A., Ramsden, S. J. & Yaghi, O. M. The reticular chemistry structure resource (RCSR) database of, and symbols for, crystal nets. Acc. Chem. Res. 41, 1782–1789 (2008).

    Article  CAS  Google Scholar 

  71. 71

    Xiang, Z. et al. Systematic tuning and multifunctionalization of covalent organic polymers for enhanced carbon capture. J. Am. Chem. Soc. 137, 13301–13307 (2015).

    CAS  Article  Google Scholar 

  72. 72

    Bao, Y. et al. In silico discovery of high deliverable capacity metal–organic frameworks. J. Phys. Chem. C 119, 186–195 (2015).

    CAS  Article  Google Scholar 

  73. 73

    Addicoat, M. A., Coupry, D. E. & Heine, T. AuToGraFS: automatic topological generator for framework structures. J. Phys. Chem. A 118, 9607–9614 (2014).

    CAS  Article  Google Scholar 

  74. 74

    Gale, J. GULP: a computer program for the symmetry-adapted simulation of solids. J. Chem. Soc. Faraday Trans. 93, 629–637 (1997).

    CAS  Article  Google Scholar 

  75. 75

    Coupry, D. E., Addicoat, M. A. & Heine, T. Extension of the universal force field for metal–organic frameworks. J. Chem. Theory Comput. 12, 5215–5225 (2016).

    CAS  Article  Google Scholar 

  76. 76

    Addicoat, M. A., Vankova, N., Akter, I. F. & Heine, T. Extension of the universal force field to metal–organic frameworks. J. Chem. Theory Comput. 10, 880–891 (2014).

    CAS  Article  Google Scholar 

  77. 77

    Boyd, P. G. & Woo, T. K. A generalized method for constructing hypothetical nanoporous materials of any net topology from graph theory. CrystEngComm 18, 3777–3792 (2016).

    CAS  Article  Google Scholar 

  78. 78

    US Department of Energy. ARPA-E methane opportunities for vehicular energy (MOVE) (DE-FOA-000672). ARPAhttp://arpa-e-foa.energy.gov (2012).

  79. 79

    Hulvey, Z. et al. Critical factors driving the high volumetric uptake of methane in Cu3(btc)2 . J. Am. Chem. Soc. 137, 10816–10825 (2015).

    CAS  Article  Google Scholar 

  80. 80

    Kim, J., Lin, L.-C., Lee, K., Neaton, J. B. & Smit, B. Efficient determination of accurate force fields for porous materials using ab initio total energy calculations. J. Phys. Chem. C 118, 2693–2701 (2014).

    CAS  Article  Google Scholar 

  81. 81

    Becker, T. M., Heinen, J., Dubbeldam, D., Lin, L.-C. & Vlugt, T. J. H. Polarizable force fields for CO2 and CH4 adsorption in M-MOF-74. J. Phys. Chem. C 121, 4659–4673 (2017).

    CAS  Article  Google Scholar 

  82. 82

    Mason, J. a. et al. Methane storage in flexible metal–organic frameworks with intrinsic thermal management. Nature 527, 357–361 (2015).

    CAS  Article  Google Scholar 

  83. 83

    Wilmer, C. E., Farha, O. K., Bae, Y.-S., Hupp, J. T. & Snurr, R. Q. Structure–property relationships of porous materials for carbon dioxide separation and capture. Energy Environ. Sci. 5, 9849 (2012).

    CAS  Article  Google Scholar 

  84. 84

    Chung, Y. G. et al. In silico discovery of metal–organic frameworks for precombustion CO2 capture using a genetic algorithm. Sci. Adv. 2, e1600909 (2016).

    Article  CAS  Google Scholar 

  85. 85

    Braun, E. et al. High-throughput computational screening of nanoporous adsorbents for CO2 capture from natural gas. Mol. Syst. Des. Eng. 1, 175–188 (2016).

    CAS  Article  Google Scholar 

  86. 86

    Qiao, Z., Zhang, K. & Jiang, J. In silico screening of 4764 computation-ready, experimental metal–organic frameworks for CO2 separation. J. Mater. Chem. A 4, 2105–2114 (2016).

    CAS  Article  Google Scholar 

  87. 87

    Rufford, T. E. et al. The removal of CO2 and N2 from natural gas: a review of conventional and emerging process technologies. J. Pet. Sci. Eng. 9495, 123–154 (2012).

    Article  CAS  Google Scholar 

  88. 88

    Lee, Z. H., Lee, K. T., Bhatia, S. & Mohamed, A. R. Post-combustion carbon dioxide capture: evolution towards utilization of nanomaterials. Renew. Sustain. Energy Rev. 16, 2599–2609 (2012).

    CAS  Article  Google Scholar 

  89. 89

    IEA Statistics. CO2 emissions from fuel combustion — highlights. IEAhttp://www.pbl.nl/en/publications/2012/co2-emissions-from-fuel-combustion-2012-edition (2012).

  90. 90

    Abu-Zahra, M. R. M., Schneiders, L. H. J., Niederer, J. P. M., Feron, P. H. M. & Versteeg, G. F. CO2 capture from power plants. Int. J. Greenh. Gas Control 1, 37–46 (2007).

    CAS  Article  Google Scholar 

  91. 91

    Bae, Y.-S. & Snurr, R. Q. Development and evaluation of porous materials for carbon dioxide separation and capture. Angew. Chem. Int. Ed. 50, 11586–11596 (2011).

    CAS  Article  Google Scholar 

  92. 92

    Chen, T.-H. et al. Mesoporous fluorinated metal–organic frameworks with exceptional adsorption of fluorocarbons and CFCs. Angew. Chem. Int. Ed. 54, 13902–13906 (2015).

    CAS  Article  Google Scholar 

  93. 93

    Pachfule, P., Chen, Y., Sahoo, S. C., Jiang, J. & Banerjee, R. Structural isomerism and effect of fluorination on gas adsorption in copper-tetrazolate based metal organic frameworks. Chem. Mater. 23, 2908–2916 (2011).

    CAS  Article  Google Scholar 

  94. 94

    Makal, T. A., Wang, X. & Zhou, H.-C. Tuning the moisture and thermal stability of metal–organic frameworks through incorporation of pendant hydrophobic groups. Cryst. Growth Des. 13, 4760–4768 (2013).

    CAS  Article  Google Scholar 

  95. 95

    Li, Z., Xiao, G., Yang, Q., Xiao, Y. & Zhong, C. Computational exploration of metal–organic frameworks for CO2/CH4 separation via temperature swing adsorption. Chem. Eng. Sci. 120, 59–66 (2014).

    CAS  Article  Google Scholar 

  96. 96

    Tong, M., Yang, Q., Xiao, Y. & Zhong, C. Revealing the structure–property relationship of covalent organic frameworks for CO2 capture from postcombustion gas: a multi-scale computational study. Phys. Chem. Chem. Phys. 16, 15189 (2014).

    CAS  Article  Google Scholar 

  97. 97

    Simon, C. M., Mercado, R., Schnell, S. K., Smit, B. & Haranczyk, M. What are the best materials to separate a xenon/krypton mixture? Chem. Mater. 27, 4459–4475 (2015).

    CAS  Article  Google Scholar 

  98. 98

    Banerjee, D. et al. Metal–organic framework with optimally selective xenon adsorption and separation. Nat. Commun. 7, 11831 (2016).

    Article  CAS  Google Scholar 

  99. 99

    Sikora, B. J., Wilmer, C. E., Greenfield, M. L. & Snurr, R. Q. Thermodynamic analysis of Xe/Kr selectivity in over 137 000 hypothetical metal–organic frameworks. Chem. Sci. 3, 2217 (2012).

    CAS  Article  Google Scholar 

  100. 100

    Sumer, Z. & Keskin, S. Molecular simulations of MOF adsorbents and membranes for noble gas separations. Chem. Eng. Sci. 164, 108–121 (2017).

    CAS  Article  Google Scholar 

  101. 101

    Gee, J. A. et al. Computational identification and experimental evaluation of metal–organic frameworks for xylene enrichment. J. Phys. Chem. C 120, 12075–12082 (2016).

    CAS  Article  Google Scholar 

  102. 102

    Thornton, A. W. et al. Materials genome in action: identifying the performance limits of physical hydrogen storage. Chem. Mater. 29, 2844–2854 (2017).

    CAS  Article  Google Scholar 

  103. 103

    Bobbitt, N. S., Chen, J. & Snurr, R. Q. High-throughput screening of metal–organic frameworks for hydrogen storage at cryogenic temperature. J. Phys. Chem. C 120, 27328–27341 (2016).

    CAS  Article  Google Scholar 

  104. 104

    Colón, Y. J., Fairen-Jimenez, D., Wilmer, C. E. & Snurr, R. Q. High-throughput screening of porous crystalline materials for hydrogen storage capacity near room temperature. J. Phys. Chem. C 118, 5383–5389 (2014).

    Article  CAS  Google Scholar 

  105. 105

    U.S. Department of Energy. Targets for onboard hydrogen storage systems for light-duty vehicles. Energy.govhttps://energy.gov/sites/prod/files/2015/05/f22/fcto_targets_onboard_hydro_storage_explanation.pdf (2015).

  106. 106

    McDaniel, J. G., Li, S., Tylianakis, E., Snurr, R. Q. & Schmidt, J. R. Evaluation of force field performance for high-throughput screening of gas uptake in metal–organic frameworks. J. Phys. Chem. C 119, 3143–3152 (2015).

    CAS  Article  Google Scholar 

  107. 107

    McDaniel, J. G. & Schmidt, J. R. Robust, transferable, and physically motivated force fields for gas adsorption in functionalized zeolitic imidazolate frameworks. J. Phys. Chem. C 116, 14031–14039 (2012).

    CAS  Article  Google Scholar 

  108. 108

    Mercado, R. et al. Force field development from periodic density functional theory calculations for gas separation applications using metal–organic frameworks. J. Phys. Chem. C 120, 12590–12604 (2016).

    CAS  Article  Google Scholar 

  109. 109

    Rappe, A. K. & Goddard, W. A. Charge equilibration for molecular dynamics simulations. J. Phys. Chem. 95, 3358–3363 (1991).

    CAS  Article  Google Scholar 

  110. 110

    Wilmer, C. E. & Snurr, R. Q. Towards rapid computational screening of metal–organic frameworks for carbon dioxide capture: calculation of framework charges via charge equilibration. Chem. Eng. J. 171, 775–781 (2011).

    CAS  Article  Google Scholar 

  111. 111

    Wilmer, C. E., Kim, K. C. & Snurr, R. Q. An extended charge equilibration method. J. Phys. Chem. Lett. 3, 2506–2511 (2012).

    CAS  Article  Google Scholar 

  112. 112

    Haldoupis, E., Nair, S. & Sholl, D. S. Finding MOFs for highly selective CO2/N2 adsorption using materials screening based on efficient assignment of atomic point charges. J. Am. Chem. Soc. 134, 4313–4323 (2012).

    CAS  Article  Google Scholar 

  113. 113

    Wells, B. A., De Bruin-Dickason, C. & Chaffee, A. L. Charge equilibration based on atomic ionization in metal–organic frameworks. J. Phys. Chem. C 119, 456–466 (2015).

    CAS  Article  Google Scholar 

  114. 114

    Moghadam, P. Z., Fairen-Jimenez, D. & Snurr, R. Q. Efficient identification of hydrophobic MOFs: application in the capture of toxic industrial chemicals. J. Mater. Chem. A 4, 529–536 (2016).

    CAS  Article  Google Scholar 

  115. 115

    Kadantsev, E. S., Boyd, P. G., Daff, T. D. & Woo, T. K. Fast and accurate electrostatics in metal organic frameworks with a robust charge equilibration parameterization for high-throughput virtual screening of gas adsorption. J. Phys. Chem. Lett. 4, 3056–3061 (2013).

    CAS  Article  Google Scholar 

  116. 116

    Campanñá, C., Mussard, B. & Woo, T. K. Electrostatic potential derived atomic charges for periodic systems using a modified error functional. J. Chem. Theory Comput. 5, 2866–2878 (2009).

    Article  CAS  Google Scholar 

  117. 117

    Manz, T. A. & Sholl, D. S. Chemically meaningful atomic charges that reproduce the electrostatic potential in periodic and nonperiodic materials. J. Chem. Theory Comput. 6, 2455–2468 (2010).

    CAS  Article  Google Scholar 

  118. 118

    Manz, T. A. & Sholl, D. S. Improved atoms-in-molecule charge partitioning functional for simultaneously reproducing the electrostatic potential and chemical states in periodic and nonperiodic materials. J. Chem. Theory Comput. 8, 2844–2867 (2012).

    CAS  Article  Google Scholar 

  119. 119

    Nazarian, D., Camp, J. S. & Sholl, D. S. A. Comprehensive set of high-quality point charges for simulations of metal–organic frameworks. Chem. Mater. 28, 785–793 (2016).

    CAS  Article  Google Scholar 

  120. 120

    First, E. L., Gounaris, C. E., Wei, J. & Floudas, C. A. Computational characterization of zeolite porous networks: an automated approach. Phys. Chem. Chem. Phys. 13, 17339–17358 (2011).

    CAS  Article  Google Scholar 

  121. 121

    Sarkisov, L. & Harrison, A. Computational structure characterisation tools in application to ordered and disordered porous materials. Mol. Simul. 37, 1248–1257 (2011).

    CAS  Article  Google Scholar 

  122. 122

    Aghaji, M. Z., Fernandez, M., Boyd, P. G., Daff, T. D. & Woo, T. K. Quantitative structure–property relationship models for recognizing metal organic frameworks (MOFs) with high CO2 working capacity and CO2/CH4 selectivity for methane purification. Eur. J. Inorg. Chem. 2016, 4505–4511 (2016).

    CAS  Article  Google Scholar 

  123. 123

    Fernandez, M., Boyd, P. G., Daff, T. D., Aghaji, M. Z. & Woo, T. K. Rapid and accurate machine learning recognition of high performing metal organic frameworks for CO2 capture. J. Phys. Chem. Lett. 5, 3056–3060 (2014).

    CAS  Article  Google Scholar 

  124. 124

    Fernandez, M. & Barnard, A. S. Geometrical properties can predict CO2 and N2 adsorption performance of metal–organic frameworks (MOFs) at low pressure. ACS Comb. Sci. 18, 243–252 (2016).

    CAS  Article  Google Scholar 

  125. 125

    Fernandez, M., Trefiak, N. R. & Woo, T. K. Atomic property weighted radial distribution functions descriptors of metal–organic frameworks for the prediction of gas uptake capacity. J. Phys. Chem. C 117, 14095–14105 (2013).

    CAS  Article  Google Scholar 

  126. 126

    Thornton, A. W., Winkler, D. A., Liu, M. S., Haranczyk, M. & Kennedy, D. F. Towards computational design of zeolite catalysts for CO2 reduction. RSC Adv. 5, 44361–44370 (2015).

    CAS  Article  Google Scholar 

  127. 127

    Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016).

    CAS  Article  Google Scholar 

  128. 128

    Evans, J. D. et al. Computational identification of organic porous molecular crystals. CrystEngComm 18, 4133–4141 (2016).

    CAS  Article  Google Scholar 

  129. 129

    Fernandez, M., Woo, T. K., Wilmer, C. E. & Snurr, R. Q. Large-scale quantitative structure–property relationship (QSPR) analysis of methane storage in metal–organic frameworks. J. Phys. Chem. C 117, 7681–7689 (2013).

    CAS  Article  Google Scholar 

  130. 130

    Lee, Y. et al. Quantifying similarity of pore-geometry in nanoporous materials. Nat. Commun. 8, 15396 (2017).

    CAS  Article  Google Scholar 

  131. 131

    Boyd, P. G., Moosavi, S. M., Witman, M. & Smit, B. Force-field prediction of materials properties in metal–organic frameworks. J. Phys. Chem. Lett. 8, 357–363 (2017).

    CAS  Article  Google Scholar 

  132. 132

    Springer, S. et al. A zeolitic imidazolate framework with conformational variety: conformational polymorphs versus frameworks with static conformational disorder. CrystEngComm 18, 2477–2489 (2016).

    CAS  Article  Google Scholar 

  133. 133

    Sarkisov, L., Martin, R. L., Haranczyk, M. & Smit, B. On the flexibility of metal–organic frameworks. J. Am. Chem. Soc. 136, 2228–2231 (2014).

    CAS  Article  Google Scholar 

  134. 134

    Watanabe, T. & Sholl, D. S. Accelerating applications of metal–organic frameworks for gas adsorption and separation by computational screening of materials. Langmuir 28, 14114–14128 (2012).

    CAS  Article  Google Scholar 

  135. 135

    Nazarian, D., Camp, J. S., Chung, Y. G., Snurr, R. Q. & Sholl, D. S. Large-scale refinement of metal–organic framework structures using density functional theory. Chem. Mater. 29, 2521–2528 (2017).

    CAS  Article  Google Scholar 

  136. 136

    Witman, M. et al. The influence of intrinsic framework flexibility on adsorption in nanoporous materials. J. Am. Chem. Soc. 139, 5547–5557 (2017).

    CAS  Article  Google Scholar 

  137. 137

    Krause, S. et al. A pressure-amplifying framework material with negative gas adsorption transitions. Nature 532, 348–352 (2016).

    CAS  Article  Google Scholar 

  138. 138

    Serre, C., Bourrelly, S., Ramsahye, N. A. & Maurin, G. An explanation for the very large breathing effect of a metal–organic framework during CO2 adsorption. Adv. Mater. 19, 2246–2251 (2007).

    CAS  Article  Google Scholar 

  139. 139

    Barthelet, K., Marrot, J. J., Riou, D. & Férey, G. A breathing hybrid organic–inorganic solid with very large pores and high magnetic characteristics. Angew. Chem. Int. Ed. 41, 281–284 (2002).

    CAS  Article  Google Scholar 

  140. 140

    Serre, C. et al. Very large breathing effect in the first nanoporous chromium(III)-based solids: MIL-53 or CrIII(OH)·{O2C–C6H4–CO2}·{HO2C–C6H4–CO2H}x·H2Oy. J. Am. Chem. Soc. 124, 13519–13526 (2002).

    CAS  Article  Google Scholar 

  141. 141

    Barthelet, K., Marrot, J., Ferey, G. & Riou, D. VIII(OH){O2C–C6H4–CO2}·(HO2C–C6H4–CO2H)x (DMF)y(H2O)z (or MIL-68), a new vanadocarboxylate with a large pore hybrid topology: reticular synthesis with infinite inorganic building blocks? Chem. Commun. 2004, 520–521 (2004).

    Article  Google Scholar 

  142. 142

    Tan, J. C. & Cheetham, A. K. Mechanical properties of hybrid inorganic–organic framework materials: establishing fundamental structure–property relationships. Chem. Soc. Rev. 40, 1059–1080 (2011).

    CAS  Article  Google Scholar 

  143. 143

    Li, S., Chung, Y. G. & Snurr, R. Q. High-throughput screening of metal–organic frameworks for CO2 capture in the presence of water. Langmuir 32, 10368–10376 (2016).

    CAS  Article  Google Scholar 

  144. 144

    Greathouse, J. A. & Allendorf, M. D. The interaction of water with MOF-5 simulated by molecular dynamics. J. Am. Chem. Soc. 128, 10678–10679 (2006).

    CAS  Article  Google Scholar 

  145. 145

    Haigis, V., Coudert, F.-X., Vuilleumier, R., Boutin, A. & Fuchs, A. H. Hydrothermal breakdown of flexible metal–organic frameworks: a study by first-principles molecular dynamics. J. Phys. Chem. Lett. 6, 4365–4370 (2015).

    CAS  Article  Google Scholar 

  146. 146

    Chanut, N. et al. Screening the effect of water vapour on gas adsorption performance: application to CO2 capture from flue gas in metal–organic frameworks. ChemSusChem 10, 1543–1553 (2017).

    CAS  Article  Google Scholar 

  147. 147

    Bellarosa, L., Gutiérrez-Sevillano, J. J., Calero, S. & López, N. How ligands improve the hydrothermal stability and affect the adsorption in the IRMOF family. Phys. Chem. Chem. Phys. 15, 17696–17704 (2013).

    CAS  Article  Google Scholar 

  148. 148

    Vanduyfhuys, L. et al. QuickFF: a program for a quick and easy derivation of force fields for metal–organic frameworks from ab initio input. J. Comput. Chem. 36, 1015–1027 (2015).

    CAS  Article  Google Scholar 

  149. 149

    Wieme, J., Vanduyfhuys, L., Rogge, S. M. J., Waroquier, M. & Van Speybroeck, V. Exploring the flexibility of MIL-47(V)-type materials using force field molecular dynamics simulations. J. Phys. Chem. C 120, 14934–14947 (2016).

    CAS  Article  Google Scholar 

  150. 150

    Bureekaew, S. et al. MOF-FF — a flexible first-principles derived force field for metal–organic frameworks. Phys. Status Solidi 250, 1128–1141 (2013).

    CAS  Article  Google Scholar 

  151. 151

    Bristow, J. K., Tiana, D. & Walsh, A. Transferable force field for metal–organic frameworks from first-principles: BTW-FF. J. Chem. Theory Comput. 10, 4644–4652 (2014).

    CAS  Article  Google Scholar 

  152. 152

    Bristow, J. K., Skelton, J. M., Svane, K. L., Walsh, A. & Gale, J. D. A general forcefield for accurate phonon properties of metal–organic frameworks. Phys. Chem. Chem. Phys. 18, 29316–29329 (2016).

    CAS  Article  Google Scholar 

  153. 153

    Jeong, W. & Kim, J. Understanding the mechanisms of CO2 adsorption enhancement in pure silica zeolites under humid conditions. J. Phys. Chem. C 120, 23500–23510 (2016).

    CAS  Article  Google Scholar 

  154. 154

    Poloni, R. & Kim, J. Predicting low-k zeolite materials. J. Mater. Chem. C 2, 2298 (2014).

    CAS  Article  Google Scholar 

  155. 155

    Gomez, D. A., Toda, J. & Sastre, G. Screening of hypothetical metal–organic frameworks for H2 storage. Phys. Chem. Chem. Phys. 16, 19001–19010 (2014).

    CAS  Article  Google Scholar 

  156. 156

    Qiao, Z., Peng, C., Zhou, J. & Jiang, J. High-throughput computational screening of 137953 metal–organic frameworks for membrane separation of a CO2/N2/CH4 mixture. J. Mater. Chem. A 4, 15904–15912 (2016).

    CAS  Article  Google Scholar 

  157. 157

    First, E. L., Gounaris, C. E. & Floudas, C. A. Predictive framework for shape-selective separations in three-dimensional zeolites and metal–organic frameworks. Langmuir 29, 5599–5608 (2013).

    CAS  Article  Google Scholar 

  158. 158

    Witman, M. et al. In silico design and screening of hypothetical MOF-74 analogs and their experimental synthesis. Chem. Sci. 7, 6263–6272 (2016).

    CAS  Article  Google Scholar 

  159. 159

    Yeo, B. C., Kim, D., Kim, H. & Han, S. S. High-throughput screening to investigate the relationship between the selectivity and working capacity of porous materials for propylene/propane adsorptive separation. J. Phys. Chem. C 120, 24224–24230 (2016).

    CAS  Article  Google Scholar 

  160. 160

    Van Heest, T., Teich-McGoldrick, S. L., Greathouse, J. A., Allendorf, M. D. & Sholl, D. S. Identification of metal–organic framework materials for adsorption separation of rare gases: applicability of ideal adsorbed solution theory (IAST) and effects of inaccessible framework regions. J. Phys. Chem. C 116, 13183–13195 (2012).

    CAS  Article  Google Scholar 

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Acknowledgements

The intial stage of this work was supported by the Center for Gas Separations Relevant to Clean Energy Technologies, an Energy Frontier Research Center funded by the US Department of Energy, Office of Science, Basic Energy Sciences (DE-SC0001015). This work was further supported by the Swiss National Science Foundation through the National Center of Competence in Research (NCCR) Materials’ Revolution: Computational Design and Discovery of Novel Materials (MARVEL). In addition, Y.L. is supported by the Korean–Swiss Science and Technology Programme (KSSTP, Grant No. 162130), and P.G.B. and B.S. are supported by the European Research Council under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 666983, MaGic).

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Boyd, P., Lee, Y. & Smit, B. Computational development of the nanoporous materials genome. Nat Rev Mater 2, 17037 (2017). https://doi.org/10.1038/natrevmats.2017.37

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