2,890
Views
70
CrossRef citations to date
0
Altmetric
Articles

The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separationFootnote*

, , , , , , , , , , & show all
Pages 1082-1121 | Received 15 Mar 2019, Accepted 15 Jul 2019, Published online: 08 Aug 2019

References

  • Furukawa H, Cordova KE, O'Keeffe M, et al. The chemistry and applications of metal-organic frameworks. Science. 2013;341:1230444.
  • Farha OK, Eryazici I, Jeong NC, et al. Metal-organic framework materials with ultrahigh surface areas: is the sky the limit? J Am Chem Soc. 2012;134:15016–15021.
  • Morris RE, Wheatley PS. Gas storage in nanoporous materials. Angew Chem Int Ed. 2008;47:4966–4981.
  • Li J-R, Kuppler RJ, Zhou H-C. Selective gas adsorption and separation in metal-organic frameworks. Chem Soc Rev. 2009;38:1477–1504.
  • Kreno LE, Leong K, Farha OK, et al. Metal-organic framework materials as chemical sensors. Chem Rev. 2012;112:1105–1125.
  • Li H, Eddaoudi M, O'Keeffe M, et al. Design and synthesis of an exceptionally stable and highly porous metal-organic framework. Nature. 1999;402:276–279.
  • Notman N. MOFs find a use. Chemistry World 2017 [cited 2019 Apr 1]. Available from: https://www.chemistryworld.com/features/mofs-find-a-use/2500508.article
  • Hendon CH, Rieth AJ, Korzyński MD, et al. Grand challenges and future opportunities for metal-organic frameworks. ACS Cent Sci. 2017;3:554–563.
  • Howarth AJ, Liu Y, Li P, et al. Chemical, thermal and mechanical stabilities of metal-organic frameworks. Nat Rev Mater. 2016;1:15018.
  • Rosi NL, Kim J, Eddaoudi M, et al. Rod packings and metal-organic frameworks constructed from rod-shaped secondary building units. J Am Chem Soc. 2005;127:1504–1518.
  • Chui SS-Y, Lo SM-F, Charmant JP, et al. A chemically functionalizable nanoporous material [Cu3(TMA)2(H2O)3]n. Science. 1999;283:1148–1150.
  • Eddaoudi M, Kim J, Rosi N, et al. Systematic design of pore size and functionality in isoreticular MOFs and their application in methane storage. Science. 2002;295:469–472.
  • Simon CM, Kim J, Gómez-Gualdrón DA, et al. Computer-aided search for materials to store natural gas for vehicles. Front Young Minds. 2015;3:11.
  • Chung YG, Camp J, Haranczyk M, et al. Computation-ready, experimental metal-organic frameworks: a tool to enable high-throughput screening of nanoporous crystals. Chem Mater. 2014;26:6185–6192.
  • Moghadam PZ, Li A, Wiggin SB, et al. Development of a Cambridge structural database subset: a collection of metal-organic frameworks for past, present, and future. Chem Mater. 2017;29:2618–2625.
  • Stock N, Biswas S. Synthesis of metal-organic frameworks (MOFs): routes to various MOF topologies, morphologies, and composites. Chem Rev. 2011;112:933–969.
  • Cordova KE, Yaghi OM. The ‘folklore’ and reality of reticular chemistry. Mater Chem Front. 2017;1:1304–1309.
  • Schoedel A, Ji Z, Yaghi OM. The role of metal-organic frameworks in a carbon-neutral energy cycle. Nat Energy. 2016;1:16034.
  • Mason JA, Veenstra M, Long JR. Evaluating metal-organic frameworks for natural gas storage. Chem Sci. 2014;5:32–51.
  • Moosavi SM, Chidambaram A, Talirz L, et al. Capturing chemical intuition in synthesis of metal-organic frameworks. Nat Commun. 2019;10:539.
  • Greenaway RL, Santolini V, Bennison MJ, et al. High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis. Nat Commun. 2018;9:2849.
  • Steiner S, Wolf J, Glatzel S, et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science. 2019;363:eaav2211.
  • Häse F, Roch LM, Aspuru-Guzik A. Next-generation experimentation with self-driving laboratories. Trends Chem. 2019;1:282–291.
  • Jain A, Ong SP, Hautier G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 2013;1:011002.
  • Boyd PG, Lee Y, Smit B. Computational development of the nanoporous materials genome. Nat Rev Mater. 2017;2:17037.
  • Fraux G, Chibani S, Coudert F-X. Modelling of framework materials at multiple scales: current practices and open questions. Philos Trans R Soc A. 2019;377:20180220.
  • Colón YJ, Snurr RQ. High-throughput computational screening of metal-organic frameworks. Chem Soc Rev. 2014;43:5735–5749.
  • Rappé AK, Casewit CJ, Colwell KS, et al. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc. 1992;114:10024–10035.
  • Mayo SL, Olafson BD, Goddard W. a generic force field for molecular simulations. J Phys Chem. 1990;94:8897–8909.
  • Martin MG, Siepmann JI. Transferable potentials for phase equilibria. 1. United-atom description of n-alkanes. J Phys Chem B. 1998;102:2569–2577.
  • Potoff JJ, Siepmann JI. Vapor–liquid equilibria of mixtures containing alkanes, carbon dioxide, and nitrogen. AIChE J. 2001;47:1676–1682.
  • Ongari D, Boyd PG, Kadioglu O, et al. Evaluating charge equilibration methods to generate electrostatic fields in nanoporous materials. J Chem Theory Comput. 2019;15:382–401.
  • Fischer M, Gomes JR, Jorge M. Computational approaches to study adsorption in MOFs with unsaturated metal sites. Mol Simul. 2014;40:537–556.
  • Zhang Y, Li B, Krishna R, et al. Highly selective adsorption of ethylene over ethane in a MOF featuring the combination of open metal site and π-complexation. Chem Commun. 2015;51:2714–2717.
  • Uchida S, Kawamoto R, Tagami H, et al. Highly selective sorption of small unsaturated hydrocarbons by nonporous flexible framework with silver ion. J Am Chem Soc. 2008;130:12370–12376.
  • Fischer M, Gomes JR, Fröba M, et al. Modeling adsorption in metal-organic frameworks with open metal sites: propane/propylene separations. Langmuir. 2012;28:8537–8549.
  • Kulkarni AR, Sholl DS. Screening of copper open metal site MOFs for olefin/paraffin separations using DFT-derived force fields. J Phys Chem C. 2016;120:23044–23054.
  • Fischer M, Kuchta B, Firlej L, et al. Accurate prediction of hydrogen adsorption in metal- organic frameworks with unsaturated metal sites via a combined density-functional theory and molecular mechanics approach. J Phys Chem C. 2010;114:19116–19126.
  • Fischer M, Hoffmann F, Fröba M. New microporous materials for acetylene storage and C2H2/CO2 separation: insights from molecular simulations. Chem Phys Chem. 2010;11:2220–2229.
  • Zhang C, Lan Y, Guo X, et al. Materials genomics-guided ab initio screening of MOFs with open copper sites for acetylene storage. AIChE J. 2018;64:1389–1398.
  • Chen L, Morrison CA, Düren T. Improving predictions of gas adsorption in metal-organic frameworks with coordinatively unsaturated metal sites: model potentials, ab initio parameterization, and GCMC simulations. J Phys Chem C. 2012;116:18899–18909.
  • Ongari D, Tiana D, Stoneburner SJ, et al. Origin of the strong interaction between polar molecules and copper (II) Paddle-wheels in metal organic frameworks. J Phys Chem C. 2017;121:15135–15144.
  • Koh HS, Rana MK, Wong-Foy AG, et al. Predicting methane storage in open-metal-site metal-organic frameworks. J Phys Chem C. 2015;119:13451–13458.
  • Campbell C, Gomes JR, Fischer M, et al. New model for predicting adsorption of polar molecules in metal-organic frameworks with unsaturated metal sites. J Phys Chem Lett. 2018;9:3544–3553.
  • Dzubak AL, Lin L-C, Kim J, et al. Ab initio carbon capture in open-site metal-organic frameworks. Nat Chem. 2012;4:810–816.
  • Mercado R, Vlaisavljevich B, Lin L-C, et al. Force field development from periodic density functional theory calculations for gas separation applications using metal-organic frameworks. J Phys Chem C. 2016;120:12590–12604.
  • Campbell C, Ferreiro-Rangel CA, Fischer M, et al. A transferable model for adsorption in MOFs with unsaturated metal sites. J Phys Chem C. 2016;121:441–458.
  • Frenkel D, Smit B. Understanding molecular simulation: from algorithms to applications. Vol. 1. San Diego (CA): Academic Press; 2001.
  • Cho EH, Lyu Q, Lin L-C. Computational discovery of nanoporous materials for energy-and environment-related applications. Mol Simul. 2019. doi:10.1080/08927022.2019.1626990
  • Coudert F-X, Fuchs AH. Computational characterization and prediction of metal-organic framework properties. Coord Chem Rev. 2016;307:211–236.
  • Addicoat MA, Vankova N, Akter IF, et al. Extension of the universal force field to metal-organic frameworks. J Chem Theory Comput. 2014;10:880–891.
  • Bureekaew S, Amirjalayer S, Tafipolsky M, et al. MOF-FF – a flexible first-principles derived force field for metal-organic frameworks. Phys Status Solidi (b). 2013;250:1128–1141.
  • Bristow JK, Tiana D, Walsh A. Transferable force field for metal-organic frameworks from first-principles: BTW-FF. J Chem Theory Comput. 2014;10:4644–4652.
  • Boyd PG, Moosavi SM, Witman M, et al. Force-field prediction of materials properties in metal-organic frameworks. J Phys Chem Lett. 2017;8:357–363.
  • Vanduyfhuys L, Vandenbrande S, Verstraelen T, 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. 2015;36:1015–1027.
  • Vanduyfhuys L, Vandenbrande S, Wieme J, et al. Extension of the QuickFF force field protocol for an improved accuracy of structural, vibrational, mechanical and thermal properties of metal-organic frameworks. J Comput Chem. 2018;39:999–1011.
  • Heinen J, Dubbeldam D. On flexible force fields for metal-organic frameworks: recent developments and future prospects. Wiley Interdiscip Rev. 2018;8:e1363.
  • Lawler KV, Hulvey Z, Forster PM. On the importance of a precise crystal structure for simulating gas adsorption in nanoporous materials. Phys Chem Chem Phys. 2015;17:18904–18907.
  • Li C-P, Du M. Role of solvents in coordination supramolecular systems. Chem Commun. 2011;47:5958–5972.
  • Nazarian D, Camp JS, Chung YG, et al. Large-scale refinement of metal-organic framework structures using density functional theory. Chem Mater. 2017;29:2521–2528.
  • Boyd PG, Woo TK. A generalized method for constructing hypothetical nanoporous materials of any net topology from graph theory. Cryst Eng Comm. 2016;18:3777–3792.
  • Evans JD, Fraux G, Gaillac R, et al. Computational chemistry methods for nanoporous materials. Chem Mater. 2017;29:199–212.
  • Fraux G, Coudert F-X. Recent advances in the computational chemistry of soft porous crystals. Chem Commun. 2017;53:7211–7221.
  • Tijms H. Probability: a lively introduction. Cambridge: Cambridge University Press; 2017.
  • Shell MS. Thermodynamics and statistical mechanics: an integrated approach. Cambridge: Cambridge University Press; 2015.
  • Panagiotopoulos AZ. Direct determination of phase coexistence properties of fluids by Monte Carlo simulation in a new ensemble. Mol Phys. 1987;61:813–826.
  • Panagiotopoulos AZ. Adsorption and capillary condensation of fluids in cylindrical pores by Monte Carlo simulation in the Gibbs ensemble. Mol Phys. 1987;62:701–719.
  • Dubbeldam D, Calero S, Ellis DE, et al. RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials. Mol Simul. 2016;42:81–101.
  • Dubbeldam D, Torres-Knoop A, Walton KS. On the inner workings of Monte Carlo codes. Mol Simul. 2013;39:1253–1292.
  • Jeffroy M, Fuchs AH, Boutin A. Structural changes in nanoporous solids due to fluid adsorption: thermodynamic analysis and Monte Carlo simulations. Chem Commun. 2008;(28):3275–3277. doi:10.1039/b805117h
  • Mason JA, Oktawiec J, Taylor MK, et al. Methane storage in flexible metal-organic frameworks with intrinsic thermal management. Nature. 2015;527:357–361.
  • Sarkisov L. Toward rational design of metal-organic frameworks for sensing applications: efficient calculation of adsorption characteristics in zero loading regime. J Phys Chem C. 2012;116:3025–3033.
  • Sholl DS. Understanding macroscopic diffusion of adsorbed molecules in crystalline nanoporous materials via atomistic simulations. Acc Chem Res. 2006;39:403–411.
  • Dubbeldam D, Snurr RQ. Recent developments in the molecular modeling of diffusion in nanoporous materials. Mol Simul. 2007;33:305–325.
  • Karmakar T, Piaggi PM, Perego C, et al. A cannibalistic approach to grand canonical crystal growth. J Chem Theory Comput. 2018;14:2678–2683.
  • Ozcan A, Perego C, Salvalaglio M, et al. Concentration gradient driven molecular dynamics: a new method for simulations of membrane permeation and separation. Chem Sci. 2017;8:3858–3865.
  • Perego C, Salvalaglio M, Parrinello M. Molecular dynamics simulations of solutions at constant chemical potential. J Chem Phys. 2015;142:144113.
  • Sarkisov L, Monson PA. Hysteresis in Monte Carlo and molecular dynamics simulations of adsorption in porous materials. Langmuir. 2000;16:9857–9860.
  • Düren T, Jakobtorweihen S, Keil FJ, et al. Grand canonical molecular dynamics simulations of transport diffusion in geometrically heterogeneous pores. Phys Chem Chem Phys. 2003;5:369–375.
  • Delle Site L, Krekeler C, Whittaker J, et al. Molecular dynamics of open systems: construction of a mean-field particle reservoir. Adv Theory Simul. 2019;2:1900014.
  • Moghadam PZ, Rogge SM, Li A. Structure-mechanical stability relations of metal-organic frameworks via machine learning. Matter. 2019;1(1):219–234.
  • Ghoufi A, Maurin G. Hybrid Monte Carlo simulations combined with a phase mixture model to predict the structural transitions of a porous metal-organic framework material upon adsorption of guest molecules. J Phys Chem C. 2010;114:6496–6502.
  • Palmer JC, Debenedetti PG. Computer simulation of water sorption on flexible protein crystals. J Phys Chem Lett. 2012;3:2713–2718.
  • Rogge SMJ, Goeminne R, Demuynck R, et al. Modeling gas adsorption in flexible metal-organic frameworks via hybrid Monte Carlo/molecular dynamics schemes. Adv Theory Simul. 2019;2:1800177.
  • Simon C, York AH, Sturluson A, et al. PorousMaterials.jl. 2018. Available from: https://github.com/SimonEnsemble/PorousMaterials.jl
  • Gowers RJ, Farmahini AH, Friedrich D, et al. Automated analysis and benchmarking of GCMC simulation programs in application to gas adsorption. Mol Simul. 2018;44:309–321.
  • Grossfield A, Patrone PN, Roe DR, et al. Best practices for quantification of uncertainty and sampling quality in molecular simulations [Article v1.0]. Living J Comput Mol Sci. 2019;1:5067.
  • Wilmer CE, Farha OK, Bae Y-S, et al. Structure–property relationships of porous materials for carbon dioxide separation and capture. Energy Environ Sci. 2012;5:9849–9856.
  • Simon CM, Kim J, Gómez-Gualdrón DA, et al. The materials genome in action: identifying the performance limits for methane storage. Energy Environ Sci. 2015;8:1190–1199.
  • Frenkel D, Mooij GCAM, Smit B. Novel scheme to study structural and thermal properties of continuously deformable molecules. J Phys. 1992;4:3053–3076.
  • Becker TM, Heinen J, Dubbeldam D, et al. Polarizable force fields for CO2 and CH4 adsorption in M-MOF-74. J Phys Chem C. 2017;121:4659–4673.
  • Zhang H, Snurr RQ. Computational study of water adsorption in the hydrophobic metal-organic framework ZIF-8: adsorption mechanism and acceleration of the simulations. J Phys Chem C. 2017;121:24000–24010.
  • Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. Vol. 1. New York (NY): Springer; 2001.
  • Willems TF, Rycroft CH, Kazi M, et al. Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials. Microporous Mesoporous Mater. 2012;149:134–141.
  • Collins CR, Gordon GJ, von Lilienfeld OA, et al. Constant size descriptors for accurate machine learning models of molecular properties. J Chem Phys. 2018;148:241718.
  • Fernandez M, Boyd PG, Daff TD, et al. Rapid and accurate machine learning recognition of high performing metal organic frameworks for CO2 capture. J Phys Chem Lett. 2014;5:3056–3060.
  • Simon CM, Mercado R, Schnell SK, et al. What are the best materials to separate a xenon/krypton mixture? Chem Mater. 2015;27:4459–4475.
  • Bucior BJ, Bobbitt NS, Islamoglu T, et al. Energy-based descriptors to rapidly predict hydrogen storage in metal-organic frameworks. Mol Syst Des Eng. 2019;4:162–174.
  • Thornton AW, Simon CM, Kim J, et al. Materials genome in action: identifying the performance limits of physical hydrogen storage. Chem Mater. 2017;29:2844–2854.
  • Pardakhti M, Moharreri E, Wanik D, et al. Machine learning using combined structural and chemical descriptors for prediction of methane adsorption performance of metal organic frameworks (MOFs). ACS Comb Sci. 2017;19:640–645.
  • Le T, Epa VC, Burden FR, et al. Quantitative structure–property relationship modeling of diverse materials properties. Chem Rev. 2012;112:2889–2919.
  • Fernandez M, Woo TK, Wilmer CE, et al. Large-scale quantitative structure–property relationship (QSPR) analysis of methane storage in metal-organic frameworks. J Phys Chem C. 2013;117:7681–7689.
  • Fernandez M, Trefiak NR, Woo TK. Atomic property weighted radial distribution functions descriptors of metal-organic frameworks for the prediction of gas uptake capacity. J Phys Chem C. 2013;117:14095–14105.
  • Fernandez M, Barnard AS. Geometrical properties can predict CO2 and N2 adsorption performance of metal-organic frameworks (MOFs) at low pressure. ACS Comb Sci. 2016;18:243–252.
  • Borboudakis G, Stergiannakos T, Frysali M, et al. Chemically intuited, large-scale screening of MOFs by machine learning techniques. NPJ Comput Mater. 2017;3:40.
  • Anderson R, Rodgers J, Argueta E, et al. Role of pore chemistry and topology in the CO2 capture capabilities of MOFs: from molecular simulation to machine learning. Chem Mater. 2018;30:6325–6337.
  • Tsivion E, Long JR, Head-Gordon M. Hydrogen physisorption on metal-organic framework linkers and metalated linkers: a computational study of the factors that control binding strength. J Am Chem Soc. 2014;136:17827–17835.
  • Anderson G, Schweitzer B, Anderson R, et al. Attainable volumetric targets for adsorption-based hydrogen storage in porous crystals: molecular simulation and machine learning. J Phys Chem C. 2019;123:120–130.
  • Wu X, Xiang S, Su J. Understanding quantitative relationship between methane storage capacities and characteristic properties of metal organic frameworks based on machine learning. J Phys Chem C. 2019;123(14):8550–8559.
  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436.
  • Goh GB, Hodas NO, Vishnu A. Deep learning for computational chemistry. J Comput Chem. 2017;38:1291–1307.
  • Gen M, Lin L. Genetic algorithms. In: Wah BW, editor. Wiley encyclopedia of computer science and engineering. New York (NY): Wiley-Interscience; 2007. p. 1–15. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470050118.ecse169
  • Collins SP, Daff TD, Piotrkowski SS, et al. Materials design by evolutionary optimization of functional groups in metal-organic frameworks. Sci Adv. 2016;2:e1600954.
  • Chung YG, Gómez-Gualdrón DA, Li P, et al. In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm. Sci Adv. 2016;2:e1600909.
  • Bao Y, Martin RL, Simon CM, et al. In silico discovery of high deliverable capacity metal-organic frameworks. J Phys Chem C. 2015;119:186–195.
  • Doudna JA, Charpentier E. The new frontier of genome engineering with CRISPR-Cas9. Science. 2014;346:1258096.
  • Flavahan WA, Gaskell E, Bernstein BE. Epigenetic plasticity and the hallmarks of cancer. Science. 2017;357:eaal2380.
  • Barrangou R, Fremaux C, Deveau H, et al. CRISPR provides acquired resistance against viruses in prokaryotes. Science. 2007;315:1709–1712.
  • Lander ES. The heroes of CRISPR. Cell. 2016;164:18–28.
  • Mojica FJ, Díez-Villaseñor C, Soria E, et al. Biological significance of a family of regularly spaced repeats in the genomes of archaea, bacteria and mitochondria. Mol Microbiol. 2000;36:244–246.
  • Jansen R, Embden JDAV, Gaastra W, et al. Identification of genes that are associated with DNA repeats in prokaryotes. Mol Microbiol. 2002;43:1565–1575.
  • Mojica FJ, Díez-Villaseñor C, García-Martínez J, et al. Intervening sequences of regularly spaced prokaryotic repeats derive from foreign genetic elements. J Mol Evol. 2005;60:174–182.
  • Goodman L. Hypothesis-limited research. Genome Res. 1999;9:673–676.
  • Gándara F, Bennett TD. Crystallography of metal-organic frameworks. IUCrJ. 2014;1:563–570.
  • Harris KDM, Tremayne M. Crystal structure determination from powder diffraction data. Chem Mater. 1996;8:2554–2570.
  • McCusker LB, Von Dreele RB, Cox DE. Rietveld refinement guidelines, et al. J Appl Crystallogr. 1999;32:36–50.
  • Rietveld HM. A profile refinement method for nuclear and magnetic structures. J Appl Crystallogr. 1969;2:65–71.
  • Øien-Ødegaard S, Shearer GC, Wragg DS, et al. Pitfalls in metal-organic framework crystallography: towards more accurate crystal structures. Chem Soc Rev. 2017;46:4867–4876.
  • O'Keeffe M, Yaghi OM. Deconstructing the crystal structures of metal-organic frameworks and related materials into their underlying nets. Chem Rev. 2012;112:675–702.
  • Parsons S. Introduction to twinning. Acta Crystallogr Sect D. 2003;59:1995–2003.
  • Ma J, Tran LD, Matzger AJ. Toward topology prediction in Zr-based microporous coordination polymers: the role of linker geometry and flexibility. Cryst Growth Des. 2016;16:4148–4153.
  • Jiang H-L, Makal TA, Zhou H-C. Interpenetration control in metal-organic frameworks for functional applications. Coord Chem Rev. 2013;257:2232–2249.
  • Hoshino M, Khutia A, Xing H, et al. The crystalline sponge method updated. IUCrJ. 2016;3:139–151.
  • Spek AL. PLATON SQUEEZE: a tool for the calculation of the disordered solvent contribution to the calculated structure factors. Acta Crystallogr Sect C. 2015;71:9–18.
  • Winston EB, Lowell PJ, Vacek J, et al. Dipolar molecular rotors in the metal-organic framework crystal IRMOF-2. Phys Chem Chem Phys. 2008;10:5188–5191.
  • Elsaidi SK, Mohamed MH, Simon CM, et al. Effect of ring rotation upon gas adsorption in SIFSIX-3-M (M= Fe, Ni) pillared square grid networks. Chem Sci. 2017;8:2373–2380.
  • Harris KDM, Tremayne M, Lightfoot P, et al. Crystal structure determination from powder diffraction data by Monte Carlo methods. J Am Chem Soc. 1994;116:3543–3547.
  • David WI, Shankland K, van de Streek J, et al. DASH: a program for crystal structure determination from powder diffraction data. J Appl Crystallogr. 2006;39:910–915.
  • Wahiduzzaman M, Wang S, Sikora BJ, et al. Computational structure determination of novel metal-organic frameworks. Chem Commun. 2018;54:10812–10815.
  • Li P, Vermeulen NA, Malliakas CD, et al. Bottom-up construction of a superstructure in a porous uranium-organic crystal. Science. 2017;356:624–627.
  • Colón YJ, Gómez-Gualdrón DA, Snurr RQ. Topologically guided, automated construction of metal-organic frameworks and their evaluation for energy-related applications. Cryst Growth Des. 2017;17:5801–5810.
  • Deria P, Gómez-Gualdrón DA, Bury W, et al. Ultraporous, water stable, and breathing zirconium-based metal-organic frameworks with ftw topology. J Am Chem Soc. 2015;137:13183–13190.
  • Deng H, Grunder S, Cordova KE, et al. Large-pore apertures in a series of metal-organic frameworks. Science. 2012;336:1018–1023.
  • Taddei M, Casati N, Steitz DA, et al. In situ high-resolution powder X-ray diffraction study of UiO-66 under synthesis conditions in a continuous-flow microwave reactor. Cryst Eng Comm. 2017;19:3206–3214.
  • Park JH, Min Choi K, Joon Jeon H, et al. In-situ observation for growth of hierarchical metal-organic frameworks and their self-sequestering mechanism for gas storage. Sci Rep. 2015;5:12045.
  • Wu Y, Henke S, Kieslich G, et al. Time-resolved in situ X-ray diffraction reveals metal-dependent metal-organic framework formation. Angew Chem Int Ed. 2016;55:14081–14084.
  • Dill ED, Josey AA, Folmer JCW, et al. Experimental determination of the crystallization phase-boundary velocity in the halozeotype CZX-1. Chem Mater. 2013;25:3932–3940.
  • Julien PA, Užarević K, Katsenis AD, et al. In situ monitoring and mechanism of the mechanochemical formation of a microporous MOF-74 framework. J Am Chem Soc. 2016;138:2929–2932.
  • Peterson VK, Southon PD, Halder GJ, et al. Guest adsorption in the nanoporous metal-organic framework Cu3(1,3,5-Benzenetricarboxylate)2: combined in situ x-ray diffraction and vapor sorption. Chem Mater. 2014;26:4712–4723.
  • Gonzalez MI, Mason JA, Bloch ED, et al. Structural characterization of framework–gas interactions in the metal-organic framework Co2(dobdc) by in situ single-crystal X-ray diffraction. Chem Sci. 2017;8:4387–4398.
  • Walton RI, Munn AS, Guillou N, et al. Uptake of liquid alcohols by the flexible FeIII metal-organic framework MIL-53 observed by time-resolved in situ X-ray diffraction. Chem A Eur J. 2011;17:7069–7079.
  • Scherb C, Koehn R, Bein T. Sorption behavior of an oriented surface-grown MOF-film studied by in situ X-ray diffraction. J Mater Chem. 2010;20:3046–3051.
  • Allen FH. The Cambridge structural database: a quarter of a million crystal structures and rising. Acta Crystallogr Sect B. 2002;58:380–388.
  • Groom CR, Bruno IJ, Lightfoot MP, et al. The Cambridge structural database. Acta Crystallogr Sect B. 2016;72:171–179.
  • Bruno IJ, Cole JC, Edgington PR, et al. New software for searching the Cambridge structural database and visualizing crystal structures. Acta Crystallogr Sect B. 2002;58:389–397.
  • Bruno IJ, Groom CR. A crystallographic perspective on sharing data and knowledge. J Comput Aided Mol Des. 2014;28:1015–1022.
  • Batten SR, Champness NR, Chen X-M, et al. Coordination polymers, metal-organic frameworks and the need for terminology guidelines. Cryst Eng Comm. 2012;14:3001–3004.
  • Seth S, Matzger AJ. Metal-organic frameworks: examples, counterexamples, and an actionable definition. Cryst Growth Des. 2017;17:4043–4048.
  • Ockwig NW, Delgado-Friedrichs O, O'Keeffe M, et al. Reticular chemistry: occurrence and taxonomy of nets and grammar for the design of frameworks. Acc Chem Res. 2005;38:176–182.
  • Goldsmith J, Wong-Foy AG, Cafarella MJ, et al. Theoretical limits of hydrogen storage in metal-organic frameworks: opportunities and trade-offs. Chem Mater. 2013;25:3373–3382.
  • Farha OK, Hupp JT. Rational design, synthesis, purification, and activation of metal-organic framework materials. Acc Chem Res. 2010;43:1166–1175.
  • Howarth AJ, Peters AW, Vermeulen NA, et al. Best practices for the synthesis, activation, and characterization of metal-organic frameworks. Chem Mater. 2017;29:26–39.
  • Murdock CR, McNutt NW, Keffer DJ, et al. Rotating phenyl rings as a guest-dependent switch in two-dimensional metal-organic frameworks. J Am Chem Soc. 2014;136:671–678.
  • Haldoupis E, Nair S, Sholl DS. Efficient calculation of diffusion limitations in metal organic framework materials: a tool for identifying materials for kinetic separations. J Am Chem Soc. 2010;132:7528–7539.
  • Van Heest T, Teich-McGoldrick SL, Greathouse JA, et al. 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. 2012;116:13183–13195.
  • Panella B, Hirscher M, Roth S. Hydrogen adsorption in different carbon nanostructures. Carbon. 2005;43:2209–2214.
  • Brandani S, Mangano E, Sarkisov LNet. excess and absolute adsorption and adsorption of helium. Adsorption. 2016;22:261–276.
  • Myers AL, Monson PA. Physical adsorption of gases: the case for absolute adsorption as the basis for thermodynamic analysis. Adsorption. 2014;20:591–622.
  • Watanabe T, Sholl DS. Accelerating applications of metal-organic frameworks for gas adsorption and separation by computational screening of materials. Langmuir. 2012;28:14114–14128.
  • Pinheiro M, Martin RL, Rycroft CH, et al. High accuracy geometric analysis of crystalline porous materials. CrystEngComm. 2013;15:7531–7538.
  • Pinheiro M, Martin RL, Rycroft CH, et al. Characterization and comparison of pore landscapes in crystalline porous materials. J Mol Graph Model. 2013;44:208–219.
  • Computation-ready, Experimental (CoRE) MOFs download link. Available from: http://gregchung.github.io/CoRE-MOFs/
  • Watanabe T, Manz TA, Sholl DS. Accurate treatment of electrostatics during molecular adsorption in nanoporous crystals without assigning point charges to framework atoms. J Phys Chem C. 2011;115:4824–4836.
  • Walton KS, Millward AR, Dubbeldam D, et al. Understanding inflections and steps in carbon dioxide adsorption isotherms in metal-organic frameworks. J Am Chem Soc. 2008;130:406–407.
  • Rappé AK, Goddard III WA. Charge equilibration for molecular dynamics simulations. J Phys Chem. 1991;95:3358–3363.
  • Mortier WJ, Ghosh SK, Shankar S. Electronegativity-equalization method for the calculation of atomic charges in molecules. J Am Chem Soc. 1986;108:4315–4320.
  • Ramachandran S, Lenz T, Skiff W, et al. Toward an understanding of zeolite Y as a cracking catalyst with the use of periodic charge equilibration. J Phys Chem. 1996;100:5898–5907.
  • Nistor RA, Polihronov JG, Müser MH, et al. A generalization of the charge equilibration method for nonmetallic materials. J Chem Phys. 2006;125:094108.
  • Oda A, Takahashi O. Parameter determination for the charge equilibration method including third-and fourth-order terms applied to non-metallic compounds. Chem Phys Lett. 2010;495:155–159.
  • Wilmer CE, Kim KC, Snurr RQ. An extended charge equilibration method. J Phys Chem Lett. 2012;3:2506–2511.
  • Kadantsev ES, Boyd PG, Daff TD, et al. 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. 2013;4:3056–3061.
  • Wells BA, De Bruin-Dickason C, Chaffee AL. Charge equilibration based on atomic ionization in metal-organic frameworks. J Phys Chem C. 2015;119:456–466.
  • Martin-Noble GC, Reilley D, Rivas LM, et al. EQeq+ C: an empirical bond-order-corrected extended charge equilibration method. J Chem Theory Comput. 2015;11:3364–3374.
  • Collins SP, Woo TK. Split-charge equilibration parameters for generating rapid partial atomic charges in metal-organic frameworks and porous polymer networks for high-throughput screening. J Phys Chem C. 2017;121:903–910.
  • Campañá C, Mussard B, Woo TK. Electrostatic potential derived atomic charges for periodic systems using a modified error functional. J Chem Theory Comput. 2009;5:2866–2878.
  • Manz TA, Sholl DS. Chemically meaningful atomic charges that reproduce the electrostatic potential in periodic and nonperiodic materials. J Chem Theory Comput. 2010;6:2455–2468.
  • Nazarian D, Camp JS, Sholl DS. A comprehensive set of high-quality point charges for simulations of metal-organic frameworks. Chem Mater. 2016;28:785–793.
  • Coupry DE, Addicoat MA, Heine T. Extension of the universal force field for metal-organic frameworks. J Chem Theory Comput. 2016;12:5215–5225.
  • Horike S, Shimomura S, Kitagawa S. Soft porous crystals. Nat Chem. 2009;1:695–704.
  • Barthel S, Alexandrov EV, Proserpio DM, et al. Distinguishing metal-organic frameworks. Cryst Growth Des. 2018;18:1738–1747.
  • Altintas C, Avci G, Daglar H, et al. An extensive comparative analysis of two MOF databases: high-throughput screening of computation-ready MOFs for CH4 and H2 adsorption. J Mater Chem A. 2019;7:9593–9608.
  • Zarabadi-Poor P, Marek R. Comment on ‘Database for CO2 separation performances of MOFs based on computational materials screening’. ACS Appl Mater Interfaces. 2019;11:16261–16265.
  • Altintas C, Velioglu S, Keskin S. Reply to comment on ‘Database for CO2 separation performances of MOFs based on computational materials screening’. ACS Appl Mater Interfaces. 2019;11:16266–16271.
  • Diercks CS, Yaghi OM. The atom, the molecule, and the covalent organic framework. Science. 2017;355:eaal1585.
  • Hasell T, Cooper AI. Porous organic cages: soluble, modular and molecular pores. Nat Rev Mater. 2016;1:16053.
  • Tong M, Lan Y, Yang Q, et al. Exploring the structure-property relationships of covalent organic frameworks for noble gas separations. Chem Eng Sci. 2017;168:456–464.
  • Tong M, Lan Y, Qin Z, et al. Computation-ready, experimental covalent organic framework for methane delivery: screening and material design. J Phys Chem C. 2018;122:13009–13016.
  • Miklitz M, Jiang S, Clowes R, et al. Computational screening of porous organic molecules for xenon/krypton separation. J Phys Chem C. 2017;121:15211–15222.
  • Sturluson A, Huynh MT, York AHP, et al. Eigencages: learning a latent space of porous cage molecules. ACS Cent Sci. 2018;4:1663–1676.
  • Baerlocher C, McCusker LB. Database of zeolite structures [cited 2019 Apr 1]. Available from: http://www.iza-structure.org/databases/
  • Barnett BR, Gonzalez MI, Long JR. Recent progress towards light hydrocarbon separations using metal-organic frameworks. Trends Chem. 2019;1(2):159–171.
  • Getman RB, Miller JH, Wang K, et al. Metal alkoxide functionalization in metal-organic frameworks for enhanced ambient-temperature hydrogen storage. J Phys Chem C. 2010;115:2066–2075.
  • Weston MH, Farha OK, Hauser BG, et al. Synthesis and metalation of catechol-functionalized porous organic polymers. Chem Mater. 2012;24:1292–1296.
  • Energy Information Administration, U. How much carbon dioxide is produced when different fuels are burned? [cited 2019 May 15]. Available from: https://www.eia.gov/tools/faqs/faq.php?id=73&t=11
  • Wang MQ, Huang HS. A full fuel-cycle analysis of energy and emissions impacts of transportation fuels produced from natural gas. Argonne National Laboratory Technical Report; 2000 [cited 2019 May 15]. Available from: https://publications.anl.gov/anlpubs/2000/01/34988.pdf
  • Alvarez RA, Pacala SW, Winebrake JJ, et al. Greater focus needed on methane leakage from natural gas infrastructure. Proc Natl Acad Sci. 2012;109:6435–6440.
  • Osborn SG, Vengosh A, Warner NR, et al. Methane contamination of drinking water accompanying gas-well drilling and hydraulic fracturing. Proc Natl Acad Sci. 2011;108:8172–8176.
  • Davis SC, Diegel SW, Boundy RG. Transportation energy data book. Oak Ridge National Laboratory; 2009 [cited 2019 May 15]. Available from: https://info.ornl.gov/sites/publications/files/pub31202.pdf
  • Hasan MMF, Zheng AM, Karimi IA. Minimizing boil-off losses in liquefied natural gas transportation. Ind Eng Chem Res. 2009;48:9571–9580.
  • Makal TA, Li J-R, Lu W, et al. Methane storage in advanced porous materials. Chem Soc Rev. 2012;41:7761–7779.
  • Advanced Research Projects Agency- Energy (ARPA-E), MOVE (Methane Opportunities for Vehicular Energy) Program Overview. 2012 [cited 2019 June 4]. Available from: https://arpa-e.energy.gov/sites/default/files/documents/files/MOVE_ProgramOverview.pdf
  • Zhang H, Deria P, Farha OK, et al. A thermodynamic tank model for studying the effect of higher hydrocarbons on natural gas storage in metal-organic frameworks. Energy Environ Sci. 2015;8:1501–1510.
  • Wilmer CE, Leaf M, Lee CY, et al. Large-scale screening of hypothetical metal-organic frameworks. Nat Chem. 2012;4:83–89.
  • Ma S, Sun D, Simmons JM, et al. Metal-organic framework from an anthracene derivative containing nanoscopic cages exhibiting high methane uptake. J Am Chem Soc. 2008;130:1012–1016.
  • Barthelet K, Marrot J, Riou D, et al. A breathing hybrid organic-inorganic solid with very large pores and high magnetic characteristics. Angew Chem Int Ed. 2002;41:281–284.
  • Lin X, Telepeni I, Blake AJ, et al. High capacity hydrogen adsorption in Cu(II) tetracarboxylate framework materials: the role of pore size, ligand functionalization, and exposed metal sites. J Am Chem Soc. 2009;131:2159–2171.
  • Wilmer CE, Farha OK, Yildirim T, et al. Gram-scale, high-yield synthesis of a robust metal-organic framework for storing methane and other gases. Energy Environ Sci. 2013;6:1158–1163.
  • Gómez-Gualdrón DA, Gutov OV, Krungleviciute V, et al. Computational design of metal-organic frameworks based on stable zirconium building units for storage and delivery of methane. Chem Mater. 2014;26:5632–5639.
  • Wu H, Zhou W, Yildirim T. High-capacity methane storage in metal-organic frameworks M2(dhtp): the important role of open metal sites. J Am Chem Soc. 2009;131:4995–5000.
  • Gándara F, Furukawa H, Lee S, et al. High methane storage capacity in aluminum metal-organic frameworks. J Am Chem Soc. 2014;136:5271–5274.
  • Koroneos C, Dompros A, Roumbas G, et al. Life cycle assessment of hydrogen fuel production processes. Int J Hydrogen Energy. 2004;29:1443–1450.
  • Sakintuna B, Lamari-Darkrim F, Hirscher M. Metal hydride materials for solid hydrogen storage: a review. Int J Hydrogen Energy. 2007;32:1121–1140.
  • Yang J, Sudik A, Wolverton C, et al. High capacity hydrogen storage materials: attributes for automotive applications and techniques for materials discovery. Chem Soc Rev. 2010;39:656–675.
  • US Department of Energy: Office of Energy Efficiency & Renewable Energy, Technical targets for onboard hydrogen storage for light-duty vehicles. Available from: https://www.energy.gov/eere/fuelcells/doe-technical-targets-onboard-hydrogen-storage-light-duty-vehicles
  • Murray LJ, Dincă M, Long JR. Hydrogen storage in metal-organic frameworks. Chem Soc Rev. 2009;38:1294–1314.
  • Gómez-Gualdrón DA, Colón YJ, Zhang X, et al. Evaluating topologically diverse metal-organic frameworks for cryo-adsorbed hydrogen storage. Energy Environ Sci. 2016;9:3279–3289.
  • Ahmed A, Liu Y, Purewal J, et al. Balancing gravimetric and volumetric hydrogen density in MOFs. Energy Environ Sci. 2017;10:2459–2471.
  • Kaye SS, Dailly A, Yaghi OM, et al. Impact of preparation and handling on the hydrogen storage properties of Zn4O(1,4-benzenedicarboxylate)3 (MOF-5). J Am Chem Soc. 2007;129:14176–14177.
  • Rowsell JLC, Yaghi OM. Effects of functionalization, catenation, and variation of the metal oxide and organic linking units on the low-pressure hydrogen adsorption properties of metal-organic frameworks. J Am Chem Soc. 2006;128:1304–1315.
  • Denysenko D, Grzywa M, Tonigold M, et al. Elucidating gating effects for hydrogen sorption in MFU-4-type triazolate-based metal-organic frameworks featuring different pore sizes. Chem A Eur J. 2011;17:1837–1848.
  • García-Holley P, Schweitzer B, Islamoglu T, et al. Benchmark study of hydrogen storage in metal-organic frameworks under temperature and pressure swing conditions. ACS Energy Lett. 2018;3:748–754.
  • Ahmed A, Seth S, Purewal J, et al. Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks. Nat Commun. 2019;10:1568.
  • Martin RL, Lin L-C, Jariwala K, et al. Mail-order metal-organic frameworks (MOFs): designing isoreticular MOF-5 analogues comprising commercially available organic molecules. J Phys Chem C. 2013;117:12159–12167.
  • Prasad TK, Suh MP. Control of interpenetration and gas-sorption properties of metal-organic frameworks by a simple change in ligand design. Chem A Eur J. 2012;18:8673–8680.
  • Rankine D, Avellaneda A, Hill MR, et al. Control of framework interpenetration for in situ modified hydroxyl functionalised IRMOFs. Chem Commun. 2012;48:10328–10330.
  • Koh K, Van Oosterhout JD, Roy S, et al. Exceptional surface area from coordination copolymers derived from two linear linkers of differing lengths. Chem Sci. 2012;3:2429–2432.
  • Stoller JK, Panos RJ, Krachman S, et al. Oxygen therapy for patients with COPD: current evidence and the long-term oxygen treatment trial. Chest. 2010;138:179–187.
  • Raghu G, Collard HR, Egan JJ, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med. 2011;183:788–824.
  • West EJ. Error analysis and calibration techniques to determine the measurement system requirements for an LD steelmaking dynamic control application. Meas Control. 1971;4:T96–T108.
  • Widlund D, Medvedev A, Gyllenram R. Towards model-based closed-loop control of the basic oxygen steelmaking process. IFAC Proc Vol. 1998;31:69–74.
  • McGovern SJ, Yeigh Jr JH. FCC Regeneration. 1983; US Patent 4,370,222.
  • DeCoste JB, Weston MH, Fuller PE, et al. Metal-organic frameworks for oxygen storage. Angew Chem Int Ed. 2014;53:14092–14095.
  • Moghadam PZ, Islamoglu T, Goswami S et al. Computer-aided discovery of a metal-organic framework with superior oxygen uptake. Nat Commun. 2018;9:1378.
  • Dávila M, Riccardo JL, Ramirez-Pastor AJ. Exact statistical thermodynamics of alkane binary mixtures in zeolites: new interpretation of the adsorption preference reversal phenomenon from multisite-occupancy theory. Chem Phys Lett. 2009;477:402–405.
  • Sholl DS, Lively RP. Seven chemical separations: to change the world: purifying mixtures without using heat would lower global energy use, emissions and pollution–and open up new routes to resources. Nature. 2016;532:435–437.
  • Herm ZR, Bloch ED, Long JR. Hydrocarbon separations in metal-organic frameworks. Chem Mater. 2013;26:323–338.
  • Lenzen M. Life cycle energy and greenhouse gas emissions of nuclear energy: a review. Energy Convers Manag. 2008;49:2178–2199.
  • Sood DD, Patil SK. Chemistry of nuclear fuel reprocessing: current status. J Radioanal Nucl Chem. 1996;203:547–573.
  • Soelberg NR, Garn TG, Greenhalgh MR, et al. Radioactive iodine and krypton control for nuclear fuel reprocessing facilities. Sci Technol Nucl Install. 2013;2013:1–12.
  • Banerjee D, Cairns AJ, Liu J, et al. Potential of metal-organic frameworks for separation of xenon and krypton. Acc Chem Res. 2015;48:211–219.
  • Liu J, Fernandez CA, Martin PF, et al. A two-column method for the separation of Kr and Xe from process off-gases. Ind Eng Chem Res. 2014;53:12893–12899.
  • Banerjee D, Simon CM, Elsaidi SK, et al. Xenon gas separation and storage using metal-organic frameworks. Chem. 2018;4:466–494.
  • Banerjee D, Simon CM, Plonka AM, et al. Metal-organic framework with optimally selective xenon adsorption and separation. Nat Commun. 2016;7:11831.
  • Banerjee D, Zhang Z, Plonka AM, et al. A calcium coordination framework having permanent porosity and high CO2/N2 selectivity. Cryst Growth Des. 2012;12:2162–2165.
  • Sikora BJ, Wilmer CE, Greenfield ML, et al. Thermodynamic analysis of Xe/Kr selectivity in over 137,000 hypothetical metal-organic frameworks. Chem Sci. 2012;3:2217–2223.
  • Ryan P, Farha OK, Broadbelt LJ, et al. Computational screening of metal-organic frameworks for xenon/krypton separation. AIChE J. 2011;57:1759–1766.
  • Aroniadou-Anderjaska V, Figueiredo TH, Apland JP, et al. Primary brain targets of nerve agents: the role of the amygdala in comparison to the hippocampus. Neuro Toxicol. 2009;30:772–776.
  • Milatović D, Jokanović M. Pyridinium oximes as cholinesterase reactivators in the treatment of OP poisoning. In: Gupta RC, editor. Handbook of toxicology of chemical warfare agents. San Diego: Academic Press; 2009. Chap. 65, p. 985–996.
  • Agrawal M, Gallis DFS, Greathouse JA, et al. How useful are common simulants of chemical warfare agents at predicting adsorption behavior? J Phys Chem C. 2018;122:26061–26069.
  • Figueiredo TH, Apland JP, Braga MFM, et al. Acute and long-term consequences of exposure to organophosphate nerve agents in humans. Epilepsia. 2018;59:92–99.
  • Barea E, Montoro C, Navarro JAR. Toxic gas removal – metal-organic frameworks for the capture and degradation of toxic gases and vapours. Chem Soc Rev. 2014;43:5419–5430.
  • DeCoste JB, Peterson GW. Metal-organic frameworks for air purification of toxic chemicals. Chem Rev. 2014;114:5695–5727.
  • Matito-Martos I, Moghadam PZ, Li A, et al. Discovery of an optimal porous crystalline material for the capture of chemical warfare agents. Chem Mater. 2018;30:4571–4579.
  • Küsgens P, Rose M, Senkovska I, et al. Characterization of metal-organic frameworks by water adsorption. Microporous Mesoporous Mater. 2009;120:325–330.
  • Colombo V, Galli S, Choi HJ, et al. High thermal and chemical stability in pyrazolate-bridged metal-organic frameworks with exposed metal sites. Chem Sci. 2011;2:1311–1319.
  • Smil V. Energy in the twentieth century: resources, conversions, costs, uses, and consequences. Ann Rev Energy Environ. 2000;25:21–51.
  • Ciais P, Sabine C, Bala G. Carbon and other biogeochemical cycles. In: Stocker T, Qin D, Plattner G-K, et al., editors. Climate Change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press; 2013; Chap. 6; p. 465–570.
  • Collins M, Knutti R, Arblaster J. Long-term climate change: projections, commitments and irreversibility. In: Stocker T, Qin D, Plattner G-K, et al., editors. Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press; 2013. Chap. 12, p. 1029–1136.
  • US Energy Information Administration. How much of U.S. carbon dioxide emissions are associated with electricity generation? 2019. Available from: https://www.eia.gov/tools/faqs/faq.php?id=77&t=11
  • Lal R. Carbon sequestration. Philos Trans R Soc B. 2007;363:815–830.
  • Haszeldine RS. Carbon capture and storage: how green can black be? Science. 2009;325:1647–1652.
  • Sumida K, Rogow DL, Mason JA, et al. Carbon dioxide capture in metal-organic frameworks. Chem Rev. 2012;112:724–781.
  • Whitley D. A genetic algorithm tutorial. Stat Comput. 1994;4:65–85.
  • Herm ZR, Swisher JA, Smit B, et al. Metal-organic frameworks as adsorbents for hydrogen purification and precombustion carbon dioxide capture. J Am Chem Soc. 2011;133:5664–5667.
  • McDonald TM, D'Alessandro DM, Krishna R, et al. Enhanced carbon dioxide capture upon incorporation of N, N′-dimethylethylenediamine in the metal-organic framework CuBTTri. Chem Sci. 2011;2:2022–2028.
  • Cannella WJ. Kirk-Othmer encyclopedia of chemical technology. New York (NY): Wiley Online Library; 2000.
  • Yang Y, Bai P, Guo X. Separation of xylene isomers: a review of recent advances in materials. Ind Eng Chem Res. 2017;56:14725–14753.
  • Gonzalez MI, Kapelewski MT, Bloch ED, et al. Separation of xylene isomers through multiple metal site interactions in metal-organic frameworks. J Am Chem Soc. 2018;140:3412–3422.
  • Minceva M, Rodrigues AE. Understanding and revamping of industrial scale SMB units for p-xylene separation. AIChE J. 2007;53:138–149.
  • Moïse J-C, Bellat J-P. Effect of preadsorbed water on the adsorption of p-xylene and m-xylene mixtures on BaX and BaY zeolites. J Phys Chem B. 2005;109:17239–17244.
  • Gee JA, Zhang K, Bhattacharyya S, et al. Computational identification and experimental evaluation of metal-organic frameworks for xylene enrichment. J Phys Chem C. 2016;120:12075–12082.
  • LeCun Y, Cortes C, Burges CJC. The MNIST database of handwritten digits. 1998 [cited 2019 Apr 1]. Available from: http://yann.lecun.com/exdb/mnist/
  • Wan L, Zeiler M, Zhang S. Regularization of neural networks using dropconnect. Proceedings of the 30th international conference on Machine Learning; 2013 Jun 16–21; Atlanta (GA). ACM; 2013. p. 1058–1066.
  • Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images. University of Toronto Technical Report; 2009 [cited 2019 May 15]. Available from: https://www.cs.toronto.edu/kriz/learning-features-2009-TR.pdf
  • Bennett J, Lanning S. The netflix prize. Proceedings of the KDD Cup Workshop 2007. New York; 2007. p. 3–6.
  • Bell RM, Koren Y. Lessons from the Netflix prize challenge. ACM SIGKDD Explor Newsletter. 2007;9:75–79.
  • Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer. 2009;42:30–37.
  • Koren Y. The bellkor solution to the netflix grand prize. Netflix Prize Documentation. 2009;81:1–10.
  • Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211–252.
  • Price SL. Control and prediction of the organic solid state: a challenge to theory and experiment. Proc R Soc A. 2018;474:20180351.
  • Reilly AM, Cooper RI, Adjiman CS, et al. Report on the sixth blind test of organic crystal structure prediction methods. Acta Crystallogr Sect B. 2016;72:439–459.
  • [cited 2019 Feb 5]. Available from: https://adsorption.nist.gov/factlab
  • Nguyen HGT, Espinal L, van Zee RD, et al. A reference high-pressure CO2 adsorption isotherm for ammonium ZSM-5 zeolite: Results of an interlaboratory study. Adsorption. 2018;24:531–539.
  • Siderius DW, Shen VK, Johnson III RD, editors, et al. NIST/ARPA-E database of novel and emerging adsorbent materials. National Institute of Standards and Technology: Gaithersburg, MD, 20899; 2014 [cited 2018 Dec 14]. DOI:10.18434/T43882
  • Heller SR, McNaught A, Pletnev I, et al. InChI, the IUPAC international chemical identifier. J Cheminform. 2015;7:23.
  • Britt D, Tranchemontagne D, Yaghi OM. Metal-organic frameworks with high capacity and selectivity for harmful gases. Proc Natl Acad Sci. 2008;105:11623–11627.
  • [cited 2019 Feb 5]. Available from: https://www.sigmaaldrich.com/catalog/product/aldrich/688614?lang=en&region=US
  • Siderius D, Shen V, editors. NIST registry of adsorbent materials. National Institute of Standards and Technology: Gaithersburg, MD, 20899; 2017 [cited 2018 Dec 14]. DOI:10.18434/t4/1502644.
  • Park J, Howe JD, Sholl DS. How reproducible are isotherm measurements in metal-organic frameworks? Chem Mater. 2017;29:10487–10495.
  • Simon CM, Smit B, Haranczyk M. pyIAST: ideal adsorbed solution theory (IAST) Python package. Comput Phys Commun. 2016;200:364–380.
  • Simon CM, Smit B, Haranczyk M. Documentation for pyIAST. [cited 2019 Feb 1]. Available from: https://pyiast.readthedocs.io
  • Colón YJ, Fairen-Jimenez D, Wilmer CE, et al. High-throughput screening of porous crystalline materials for hydrogen storage capacity near room temperature. J Phys Chem C. 2014;118:5383–5389.
  • Gómez-Gualdrón DA, Wilmer CE, Farha OK, et al. Exploring the limits of methane storage and delivery in nanoporous materials. J Phys Chem C. 2014;118:6941–6951.
  • [cited 2019 Feb 25]. Available from: http://www.fluidproperties.org
  • Ross RB, Ahmad R, Brennan JK, et al. The seventh industrial fluid properties simulation challenge. Fluid Phase Equil. 2014;366:136–140.
  • Ross RB, Brennan JK, Frankel KA, et al. Perfluorohexane adsorption in BCR-704 Faujasite zeolite benchmark studies for the seventh industrial fluid properties simulation challenge. Fluid Phase Equil. 2014;366:141–145.
  • Schultz NE, Riaz A, Brennan JK, et al. The eighth industrial fluid properties simulation challenge. Adsorption Sci Tech. 2016;34:3–12.
  • Ross RB, Aeschliman DB, Ahmad R, et al. Adsorption, X-ray diffraction, photoelectron, and atomic emission spectroscopy benchmark studies for the eighth industrial fluid properties simulation challenge. Adsorption Sci Tech. 2016;34:13–41.
  • Rouquerol F, Rouquerol J, Sing K. Adsorption by powders and porous solids. London: Academic Press; 1999.
  • [cited 2019 Feb 22]. Available form: https://adsorption.nist.gov/isodb/index.php#apis
  • Hall SR, Allen FH, Brown ID. The crystallographic information file (CIF): a new standard archive file for crystallography. Acta Crystallogr Sect A. 1991;A47:655–685.
  • Brown ID, McMahon B. CIF: the computer language of crystallography. Acta Crystallogr Sect B. 2002;58:317–324.
  • Curtarolo S, Hart GL, Nardelli MB, et al. The high-throughput highway to computational materials design. Nat Mater. 2013;12:191.
  • Pyzer-Knapp EO, Suh C, Gómez-Bombarelli R, et al. What is high-throughput virtual screening? A perspective from organic materials discovery. Annu Rev Mater Res. 2015;45:195–216.
  • Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel TD, et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat Mater. 2016;15:1120–1127.
  • Ceder G. Opportunities and challenges for first-principles materials design and applications to Li battery materials. MRS Bull. 2010;35:693–701.
  • Hachmann J, Olivares-Amaya R, Atahan-Evrenk S, et al. The Harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid. J Phys Chem Lett. 2011;2:2241–2251.
  • Nørskov JK, Bligaard T, Rossmeisl J, et al. Towards the computational design of solid catalysts. Nat Chem. 2009;1:37.
  • Greeley J, Jaramillo TF, Bonde J, et al. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nat Mater. 2006;5:909–913.
  • Strasser P, Fan Q, Devenney M, et al. High throughput experimental and theoretical predictive screening of materials- A comparative study of search strategies for new fuel cell anode catalysts. J Phys Chem B. 2003;107:11013–11021.
  • Kitchen DB, Decornez H, Furr JR, et al. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3:935–949.
  • Xu J, Hagler A. Chemoinformatics and Drug Discovery. Molecules. 2002;7:566–600.
  • Flaig RW, Osborn Popp TM, Fracaroli AM, et al. The chemistry of CO2 capture in an amine-functionalized metal-organic framework under dry and humid conditions. J Am Chem Soc. 2017;139:12125–12128.
  • Bachman J, Reed DA, Kapelewski MT, et al. Enabling alternative ethylene production through its selective adsorption in the metal-organic framework Mn2(m-dobdc). Energy Environ Sci. 2018;11:2423–2431.
  • Nguyen JG, Cohen SM. Moisture-resistant and superhydrophobic metal-organic frameworks obtained via postsynthetic modification. J Am Chem Soc. 2010;132:4560–4561.
  • Poloni R, Lee K, Berger RF, et al. Understanding trends in CO2 adsorption in metal-organic frameworks with open-metal sites. J Phys Chem Lett. 2014;5:861–865.
  • Coudert F-X. Responsive metal-organic frameworks and framework materials: under pressure, taking the heat, in the spotlight, with friends. Chem Mater. 2015;27:1905–1916.
  • Wieme J, Lejaeghere K, Kresse G, et al. Tuning the balance between dispersion and entropy to design temperature-responsive flexible metal-organic frameworks. Nat Commun. 2018;9:4899.
  • Coudert F-X. Reproducible research in computational chemistry of materials. Chem Mater. 2017;29:2615–2617.
  • Greathouse JA, Kinnibrugh TL, Allendorf MD. Adsorption and separation of noble gases by IRMOF-1: grand canonical Monte Carlo simulations. Ind Eng Chem Res. 2009;48:3425–3431.
  • Boutin A, Springuel-Huet M-A, Nossov A, et al. Breathing transitions in MIL-53 (Al) metal-organic framework upon xenon adsorption. Angew Chem Int Ed. 2009;48:8464–8467.
  • Gonzalez-Nelson A, Coudert F-X, van der Veen M. Rotational dynamics of linkers in metal-organic frameworks. Nanomaterials. 2019;9:330.
  • Catalano L, Naumov P. Exploiting rotational motion in molecular crystals. Cryst Eng Comm. 2018;20:5872–5883.
  • Witman M, Ling S, Jawahery S, et al. The influence of intrinsic framework flexibility on adsorption in nanoporous materials. J Am Chem Soc. 2017;139:5547–5557.
  • Krause S, Evans JD, Bon V, et al. Towards general network architecture design criteria for negative gas adsorption transitions in ultraporous frameworks. ChemRxiv. 2019. Available from: https://doi.org/10.26434/chemrxiv.7796543
  • Evans JD, Bocquet L, Coudert F-X. Origins of negative gas adsorption. Chem. 2016;1:873–886.
  • Krause S, Bon V, Senkovska I, et al. A pressure-amplifying framework material with negative gas adsorption transitions. Nature. 2016;532:348–352.
  • Lee Y, Poloni R, Kim J. Probing gas adsorption in MOFs using an efficient ab initio widom insertion Monte Carlo method. J Comput Chem. 2016;37:2808–2815.
  • Fetisov EO, Shah MS, Long JR, et al. First principles Monte Carlo simulations of unary and binary adsorption: CO2, N2, and H2O in Mg-MOF-74. Chem Commun. 2018;54:10816–10819.
  • Chen L, Grajciar L, Nachtigall P, et al. Accurate prediction of methane adsorption in a metal-organic framework with unsaturated metal sites by direct implementation of an ab initio derived potential energy surface in GCMC simulation. J Phys Chem C. 2011;115:23074–23080.
  • Cornell WD, Cieplak P, Bayly CI et al. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc. 1995;117:5179–5197.
  • Jorgensen WL, Tirado-Rives J. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc. 1988;110:1657–1666.
  • McDaniel JG, Li S, Tylianakis E, et al. Evaluation of force field performance for high-throughput screening of gas uptake in metal-organic frameworks. J Phys Chem C. 2015;119:3143–3152.
  • Jeziorski B, Moszynski R, Szalewicz K. Perturbation theory approach to intermolecular potential energy surfaces of van der Waals complexes. Chem Rev. 1994;94:1887–1930.
  • Dokur D, Keskin S. Effects of force field selection on the computational ranking of MOFs for CO2 separations. Ind Eng Chem Res. 2018;57:2298–2309.
  • Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett. 2007;98:146401.
  • Smith JS, Isayev O, Roitberg AE. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem Sci. 2017;8:3192–3203.
  • Behler J. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys Chem Chem Phys. 2011;13:17930–17955.
  • Schütt KT, Arbabzadah F, Chmiela S, et al. Quantum-chemical insights from deep tensor neural networks. Nat Commun. 2017;8:13890.
  • Bartók AP, Payne MC, Kondor R, et al. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys Rev Lett. 2010;104:136403.
  • Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw. 1989;2:359–366.
  • Lowell S, Shields JE, Thomas MA, et al. Characterization of porous solids and powders: surface area, pore size and density. Boston; Kluwer; 2004.
  • Xu Q, Zhong C. A general approach for estimating framework charges in metal- organic frameworks. J Phys Chem C. 2010;114:5035–5042.
  • Argueta E, Shaji J, Gopalan A, et al. Molecular building block-based electronic charges for high-throughput screening of metal-organic frameworks for adsorption applications. J Chem Theory Comput. 2017;14:365–376.
  • Breneman CM, Wiberg KB. Determining atom-centered monopoles from molecular electrostatic potentials. The need for high sampling density in formamide conformational analysis. J Comput Chem. 1990;11:361–373.
  • Bennett TD, Cheetham AK, Fuchs AH, et al. Interplay between defects, disorder and flexibility in metal-organic frameworks. Nat Chem. 2017;9:11–16.
  • Sholl DS, Lively RP. Defects in metal-organic frameworks: challenge or opportunity? J Phys Chem Lett. 2015;6:3437–3444.
  • Cavka JH, Jakobsen S, Olsbye U, et al. A new zirconium inorganic building brick forming metal organic frameworks with exceptional stability. J Am Chem Soc. 2008;130:13850–13851.
  • Wu H, Chua YS, Krungleviciute V, et al. Unusual and highly tunable missing-linker defects in zirconium metal-organic framework UiO-66 and their important effects on gas adsorption. J Am Chem Soc. 2013;135:10525–10532.
  • Ghosh P, Colón YJ, Snurr RQ. Water adsorption in UiO-66: the importance of defects. Chem Commun. 2014;50:11329–11331.
  • Thornton AW, Babarao R, Jain A, et al. Defects in metal-organic frameworks: a compromise between adsorption and stability? Dalton Trans. 2016;45:4352–4359.
  • Bristow JK, Svane KL, Tiana D, et al. Free energy of ligand removal in the metal-organic framework UiO-66. J Phys Chem C. 2016;120:9276–9281.
  • Zhang C, Han C, Sholl DS, et al. Computational characterization of defects in metal-organic frameworks: spontaneous and water-induced point defects in ZIF-8. J Phys Chem Lett. 2016;7:459–464.
  • Han C, Verploegh RJ, Sholl DS. Assessing the impact of point defects on molecular diffusion in ZIF-8 using molecular simulations. J Phys Chem Lett. 2018;9:4037–4044.
  • Zunger A. Beware of plausible predictions of fantasy materials. Nature. 2019;566:447–449.
  • Cantu DC, McGrail BP, Glezakou V-A. Formation mechanism of the secondary building unit in a chromium terephthalate metal-organic framework. Chem Mater. 2014;26:6401–6409.
  • Yoneya M, Tsuzuki S, Aoyagi M. Simulation of metal-organic framework self-assembly. Phys Chem Chem Phys. 2015;17:8649–8652.
  • Biswal D, Kusalik PG. Probing molecular mechanisms of self-assembly in metal-organic frameworks. ACS Nano. 2017;11:258–268.
  • Biswal D, Kusalik PG. Molecular simulations of self-assembly processes in metal-organic frameworks: model dependence. J Chem Phys. 2017;147:044702.
  • Colón YJ, Guo AZ, Antony LW, et al. Free energy of metal-organic framework self-assembly. J Chem Phys. 2019;150:104502.
  • Kollias L, Cantu DC, Tubbs MA, et al. Molecular level understanding of the free energy landscape in early stages of metal-organic framework nucleation. J Am Chem Soc. 2019;141:6073–6081.
  • Greathouse JA, Allendorf MD. The interaction of water with MOF-5 simulated by molecular dynamics. J Am Chem Soc. 2006;128:10678–10679.
  • Bellarosa L, Castillo JM, Vlugt T, et al. On the mechanism behind the instability of isoreticular metal-organic frameworks (IRMOFs) in humid environments. Chem A Eur J. 2012;18:12260–12266.
  • Han C, Zhang C, Tymińska N, et al. Insights into the stability of zeolitic imidazolate frameworks in humid acidic environments from first-principles calculations. J Phys Chem C. 2018;122:4339–4348.
  • Rogge SMJ, Waroquier M, Van Speybroeck V. Reliably modeling the mechanical stability of rigid and flexible metal-organic frameworks. Acc Chem Res. 2018;51:138–148.
  • Rajagopalan AK, Avila AM, Rajendran A. Do adsorbent screening metrics predict process performance? A process optimisation based study for post-combustion capture of CO2. Int J Greenhouse Gas Control. 2016;46:76–85.
  • First EL, Hasan MMF, Floudas CA. Discovery of novel zeolites for natural gas purification through combined material screening and process optimization. AIChE J. 2014;60:1767–1785.
  • Farmahini AH, Krishnamurthy S, Friedrich D, et al. From crystal to adsorption column: challenges in multiscale computational screening of materials for adsorption separation processes. Ind Eng Chem Res. 2018;57:15491–15511.
  • Pullumbi P, Brandani F, Brandani S. Gas separation by adsorption: technological drivers and opportunities for improvement. Curr Opin Chem Eng. 2019;24:131–142.
  • Krishna R. Methodologies for screening and selection of crystalline microporous materials in mixture separations. Sep Purif Technol. 2018;194:281–300.
  • Leperi K, Chung YG, You F, et al. Development of a general evaluation metric for rapid screening of adsorbent materials for post-combustion CO2 capture. ACS Sustain Chem Eng. 2019;7:11529–11539.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.