S. Horike, S. Shimomura, and S. Kitagawa, Soft porous crystals, Nature Chem, vol.1, pp.695-704, 2009.

F. X. Coudert, Responsive metal-organic frameworks and framework materials: Under pressure, taking the heat, in the spotlight, with friends, Chem. Mater, vol.27, pp.1905-1916, 2015.

S. Krause, V. Bon, I. Senkovska, U. Stoeck, D. Wallacher et al., A pressure-amplifying framework material with negative gas adsorption transitions, Nature, vol.532, pp.348-352, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02118754

A. B. Cairns and A. L. Goodwin, Negative linear compressibility, Phys. Chem. Chem. Phys, vol.17, pp.20449-20465, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01899684

E. C. Spencer, M. Kiran, W. Li, U. Ramamurty, N. L. Ross et al., Pressureinduced bond rearrangement and reversible phase transformation in a metal-organic framework, 2014.

, Angew. Chem. Int. Ed, vol.53, pp.5583-5586

R. Lyndon, K. Konstas, B. P. Ladewig, P. D. Southon, P. Kepert et al., Dynamic photoswitching in metal-organic frameworks as a route to low-energy carbon dioxide capture and release, Angew. Chem. Int. Ed, vol.52, pp.3695-3698, 2013.

S. H. Lapidus, G. J. Halder, P. J. Chupas, and K. W. Chapman, Exploiting high pressures to generate porosity, polymorphism, and lattice expansion in the nonporous molecular framework zn(cn), 2013.

, J. Am. Chem. Soc, vol.135, pp.7621-7628

W. D. Cornell, P. Cieplak, C. I. Bayly, I. R. Gould, K. M. Merz et al., A second generation force field for the simulation of proteins, nucleic acids, and organic molecules, J. Am. Chem. Soc, vol.117, pp.5179-5197, 1995.

A. K. Rappe, C. J. Casewit, K. S. Colwell, W. A. Goddard, and W. M. Skiff, Uff, a full periodic table force field for molecular mechanics and molecular dynamics simulations, J. Am. Chem. Soc, vol.114, pp.10024-10035, 1992.

J. A. Greathouse, N. W. Ockwig, L. J. Criscenti, T. R. Guilinger, P. Pohl et al., Computational screening of metal-organic frameworks for large-molecule chemical sensing, Phys. Chem. Chem. Phys, vol.12, p.12621, 2010.

P. Ryan, O. K. Farha, L. J. Broadbelt, and R. Q. Snurr, Computational screening of metal-organic frameworks for xenon/krypton separation, AIChE Journal, vol.57, pp.1759-1766, 2010.

Y. J. Colón and R. Q. Snurr, High-throughput computational screening of metal-organic frameworks, Chem. Soc. Rev, vol.43, pp.5735-5749, 2014.

G. Chaplais, G. Fraux, J. L. Paillaud, C. Marichal, H. Nouali et al., Impacts of the imidazolate linker substitution (CH3, cl or br) on the structural and adsorptive properties of ZIF-8, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02104808

, J. Phys. Chem. C

J. D. Howe, C. R. Morelock, Y. Jiao, K. W. Chapman, K. S. Walton et al., Understanding structure, metal distribution, and water adsorption in mixed-metal MOF-74, 2016.

, J. Phys. Chem. C, vol.121, pp.627-635

M. J. Mcgrath, J. I. Siepmann, I. Kuo, C. J. Mundy, J. Vandevondele et al., Isobaric-isothermal monte carlo simulations from first principles: Application to liquid water at ambient conditions, ChemPhysChem, vol.6, pp.1894-1901, 2005.

J. Leiding and J. D. Coe, An efficient approach to ab initio monte carlo simulation, J. Chem. Phys, vol.140, p.34106, 2014.

M. Falcioni and M. W. Deem, A biased monte carlo scheme for zeolite structure solution, J. Chem. Phys, vol.110, pp.1754-1766, 1999.

G. Maurin, P. Senet, S. Devautour, P. Gaveau, F. Henn et al., Combining the monte carlo technique with<sup>29</sup>si nmr spectroscopy: Simulations of cation locations in zeolites with various si/al ratios, J. Phys. Chem. B, vol.105, pp.9157-9161, 2001.

F. X. Coudert and A. H. Fuchs, Computational characterization and prediction of metal-organic framework properties, 2016.

, Coord. Chem. Rev, vol.307, pp.211-236

J. Paddison, S. Agrestini, M. R. Lees, C. L. Fleck, P. P. Deen et al., Spin correlations in Ca 3 Co 2 O 6 : Polarized-neutron diffraction and monte carlo study, Phys. Rev. B, vol.90, p.31, 2014.

A. F. Sapnik, H. S. Geddes, E. M. Reynolds, H. Yeung, and A. L. Goodwin, Compositional inhomogeneity and tuneable thermal expansion in mixed-metal zif-8 analogues, Chem. Commun, vol.54, pp.9651-9654, 2018.

A. B. Cairns, M. J. Cliffe, J. Paddison, D. Daisenberger, M. G. Tucker et al., Encoding complexity within supramolecular analogues of frustrated magnets, Nature Chem, vol.8, pp.442-447, 2016.

S. Bureekaew, S. Amirjalayer, M. Tafipolsky, C. Spickermann, T. K. Roy et al., MOF-FFa flexible first-principles derived force field for metal-organic frameworks, Phys. Status Solidi B, vol.250, pp.1128-1141, 2013.

L. Vanduyfhuys, S. Vandenbrande, T. Verstraelen, R. Schmid, M. Waroquier et al., QuickFF: A program for a quick and easy derivation of force fields for metal-organic frameworks fromab initioinput, J. Comput. Chem, vol.36, pp.1015-1027, 2015.

S. Impeng, R. Cedeno, J. P. Dürholt, R. Schmid, and S. Bureekaew, Computational structure prediction of (4, 4)-connected copper paddle-wheel-based MOFs: Influence of ligand functionalization on the topological preference, Cryst. Growth Des, vol.18, pp.2699-2706, 2018.

L. Vanduyfhuys, S. Vandenbrande, J. Wieme, M. Waroquier, T. Verstraelen 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, vol.39, pp.999-1011, 2018.

K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural Networks, vol.2, pp.359-366, 1989.

J. Behler and M. Parrinello, Generalized neural-network representation of high-dimensional potential-energy surfaces, Phys. Rev. Lett, vol.98, 2007.

J. S. Smith, O. Isayev, and A. E. Roitberg, ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost, Chem. Sci, vol.8, pp.3192-3203, 2017.

K. H. Cho, K. T. No, and H. A. Scheraga, A polarizable force field for water using an artificial neural network, J. Mol. Struct, vol.641, pp.77-91, 2002.

T. Morawietz and J. Behler, A density-functional theory-based neural network potential for water clusters including van der waals corrections, J. Phys. Chem. A, vol.117, pp.7356-7366, 2013.

M. Hellström and J. Behler, Neural network potentials in materials modeling, Handbook of Materials Modeling, pp.1-20, 2018.

V. L. Deringer and G. Csányi, Machine learning based interatomic potential for amorphous carbon, Phys. Rev. B, vol.95, 2017.

V. L. Deringer, N. Bernstein, A. P. Bartók, M. J. Cliffe, R. N. Kerber et al., Realistic atomistic structure of amorphous silicon from machine-learningdriven molecular dynamics, J. Phys. Chem. Lett, vol.9, pp.2879-2885, 2018.

T. D. Bennett, Y. Yue, P. Li, A. Qiao, H. Tao et al., Melt-quenched glasses of metal-organic frameworks, 2016.

, J. Am. Chem. Soc, vol.138, pp.3484-3492

Y. Zhao, S. Y. Lee, N. Becknell, O. M. Yaghi, and C. A. Angell, Nanoporous transparent mof glasses with accessible internal surface, J. Am. Chem. Soc, vol.138, pp.10818-10821, 2016.

C. Zhou, L. Longley, A. Krajnc, G. J. Smales, A. Qiao et al., Metal-organic framework glasses with permanent accessible porosity, Nature Commun, vol.9, p.19, 2018.

R. Gaillac, P. Pullumbi, K. A. Beyer, K. W. Chapman, D. A. Keen et al., Liquid metal-organic frameworks, Nature Mater, vol.16, pp.1149-1154, 2017.

A. K. Cheetham, T. D. Bennett, F. X. Coudert, and A. L. Goodwin, Defects and disorder in metal organic frameworks, 2016.

, Dalton Trans, vol.45, pp.4113-4126

R. Ginhoven, H. Jónsson, and L. R. Corrales, Silica glass structure generation forab initiocalculations using small samples of amorphous silica, Phys. Rev. B, vol.71, 2005.

J. P. Dürholt, R. Galvelis, and R. Schmid, Coarse graining of force fields for metal-organic frameworks, 2016.

, Dalton Trans, vol.45, pp.4370-4379

J. M. Vanson, F. X. Coudert, B. Rotenberg, M. Levesque, C. Tardivat et al., Unexpected coupling between flow and adsorption in porous media, Soft Matter, vol.11, pp.6125-6133, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01308089

J. D. Evans and F. X. Coudert, Macroscopic simulation of deformation in soft microporous composites, J. Phys. Chem. Lett, vol.8, pp.1578-1584, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02145005

L. Vanduyfhuys, T. Verstraelen, M. Vandichel, M. Waroquier, V. Speybroeck et al., Ab initio parametrized force field for the flexible metal-organic framework mil-53(al), 2012.

, J. Chem. Theory Comput, vol.8, pp.3217-3231

F. X. Coudert, A. H. Fuchs, and A. V. Neimark, Adsorption deformation of microporous composites, Dalton Trans, vol.45, pp.4136-4140, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02116924

E. Runge and E. Gross, Density-functional theory for time-dependent systems, Physical Review Letters, vol.52, pp.997-1000, 1984.

. Casida-me, Time-dependent density functional response theory for molecules, Recent Advances in Density Functional Methods, pp.155-192, 1995.

L. Wilbraham, F. X. Coudert, and I. Ciofini, Modelling photophysical properties of metal-organic frameworks: a density functional theory based approach, Physical Chemistry Chemical Physics, vol.18, pp.25176-25182, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02116956

X. P. Wu, L. Gagliardi, and D. G. Truhlar, Cerium metal-organic framework for photocatalysis, Journal of the American Chemical Society, vol.140, pp.7904-7912, 2018.

K. T. Butler, C. H. Hendon, and A. Walsh, Electronic chemical potentials of porous metal-organic frameworks, Journal of the American Chemical Society, vol.136, pp.2703-2706, 2014.

R. Grau-crespo, A. A. Collins, A. W. Crespo-otero, R. Hernández, N. C. Rodriguez-albelo et al., Modelling a linker mix-and-match approach for controlling the optical excitation gaps and band alignment of zeolitic imidazolate frameworks, Angewandte Chemie, vol.128, pp.16246-16250, 2016.

C. Baerlocher, L. B. Mccusker, and D. Olson, Atlas of Zeolite Framework Types 6th Edition, 2007.

V. V. Speybroeck, K. Hemelsoet, L. Joos, M. Waroquier, R. G. Bell et al., Advances in theory and their application within the field of zeolite chemistry, Chem. Soc. Rev, vol.44, pp.7044-7111, 2015.

K. W. Chapman, P. J. Chupas, and T. M. Nenoff, Radioactive iodine capture in silver-containing mordenites through nanoscale silver iodide formation, J. Am. Chem. Soc, vol.132, pp.8897-8899, 2010.

T. Bu?ko, L. Benco, J. Hafner, and J. G. Ángyán, Monomolecular cracking of propane over acidic chabazite: An ab initio molecular dynamics and transition path sampling study, J. Catal, vol.279, pp.220-228, 2011.

T. M. Nenoff, M. A. Rodriguez, N. R. Soelberg, and K. W. Chapman, Silver-mordenite for radiologic gas capture from complex streams: Dual catalytic CH3i decomposition and i confinement, Micropor. Mesopor. Mat, vol.200, pp.297-303, 2014.

Y. G. Bushuev and G. Sastre, Feasibility of pure silica zeolites, J. Phys. Chem. C, vol.114, pp.19157-19168, 2010.

V. A. Blatov, G. D. Ilyushin, and D. M. Proserpio, The zeolite conundrum: Why are there so many hypothetical zeolites and so few observed? a possible answer from the zeolite-type frameworks perceived as packings of tiles, Chem. Mater, vol.25, pp.412-424, 2013.

Y. Li, J. Yu, D. Liu, W. Yan, R. Xu et al., Design of zeolite frameworks with defined pore geometry through constrained assembly of atoms, Chem. Mater, vol.15, pp.2780-2785, 2003.

J. Yu and R. Xu, Rational approaches toward the design and synthesis of zeolitic inorganic open-framework materials, 2010.

, Acc. Chem. Res, vol.43, pp.1195-1204

Y. Li and J. Yu, New stories of zeolite structures: Their descriptions, determinations, predictions, and evaluations, Chem. Rev, vol.114, pp.7268-7316, 2014.

J. Li, A. Corma, and J. Yu, Synthesis of new zeolite structures, Chem. Soc. Rev, vol.44, pp.7112-7127, 2015.

Y. Li, J. Yu, and R. Xu, FraGen: a computer program for real-space structure solution of extended inorganic frameworks, 2012.

, J. Appl. Cryst, vol.45, pp.855-861

Y. Li, X. Li, J. Liu, F. Duan, and J. Yu, In silico prediction and screening of modular crystal structures via a high-throughput genomic approach, Nature Commun, vol.6, p.11002, 2015.

M. Treacy, K. H. Randall, S. Rao, J. A. Perry, and D. J. Chadi, Enumeration of periodic tetrahedral frameworks, Z. Kristallogr. Cryst. Mater, vol.212, p.50, 1997.

M. Treacy, I. Rivin, E. Balkovsky, K. Randall, and M. Foster, Enumeration of periodic tetrahedral frameworks. ii. polynodal graphs, Micropor. Mesopor. Mater, vol.74, pp.121-132, 2004.

M. A. Zwijnenburg and R. G. Bell, Absence of limitations on the framework density and pore size of high-silica zeolites, Chem. Mater, vol.20, pp.3008-3014, 2008.

M. A. Zwijnenburg, K. E. Jelfs, and S. T. Bromley, An extensive theoretical survey of low-density allotropy in silicon, Phys. Chem. Chem. Phys, vol.12, p.8505, 2010.

M. A. Zwijnenburg, F. Illas, and S. T. Bromley, Apparent scarcity of low-density polymorphs of inorganic solids, Phys. Rev. Lett, vol.104, p.768, 2010.

D. J. Earl and M. W. Deem, Toward a database of hypothetical zeolite structures, Ind. Eng. Chem. Res, vol.45, pp.5449-5454, 2006.

R. Pophale, P. A. Cheeseman, and M. W. Deem, A database of new zeolite-like materials, Phys. Chem. Chem. Phys, vol.13, p.12407, 2011.

J. Yu and R. Xu, Insight into the construction of open-framework aluminophosphates, Chem. Soc. Rev, vol.35, p.593, 2006.

P. Z. Moghadam, A. Li, S. B. Wiggin, A. Tao, A. Maloney et al., Development of a cambridge structural database subset: A collection of metal-organic frameworks for past, present, and future, Chem. Mater, vol.29, pp.2618-2625, 2017.

T. Watanabe and D. S. Sholl, Accelerating applications of metal-organic frameworks for gas adsorption and separation by computational screening of materials, Langmuir, vol.28, pp.14114-14128, 2012.

J. Goldsmith, A. G. Wong-foy, M. J. Cafarella, and D. J. Siegel, Theoretical limits of hydrogen storage in metal-organic frameworks: Opportunities and trade-offs, Chem. Mater, vol.25, pp.3373-3382, 2013.

Y. G. Chung, J. Camp, M. Haranczyk, B. J. Sikora, W. Bury et al., Computation-ready, experimental metal-organic frameworks: A tool to enable high-throughput screening of nanoporous crystals, Chem. Mater, vol.26, pp.6185-6192, 2014.

S. Barthel, E. V. Alexandrov, D. M. Proserpio, and B. Smit, Distinguishing metal-organic frameworks, Cryst. Growth Des, vol.18, pp.1738-1747, 2018.

S. Park, B. Kim, S. Choi, P. G. Boyd, B. Smit et al., Text mining metal-organic framework papers, J. Chem. Inf. Model, vol.58, pp.244-251, 2018.

D. Nazarian, J. S. Camp, and D. S. Sholl, A comprehensive set of high-quality point charges for simulations of metal-organic frameworks, Chem. Mater, vol.28, pp.785-793, 2016.

C. E. Wilmer, M. Leaf, C. Y. Lee, O. K. Farha, B. G. Hauser et al., Large-scale screening of hypothetical metal-organic frameworks, Nature Chem, vol.4, pp.83-89, 2011.

D. A. Gomez, J. Toda, and G. Sastre, Screening of hypothetical metal-organic frameworks for h2 storage, Phys. Chem. Chem. Phys, vol.16, pp.19001-19010, 2014.

Y. He, W. Zhou, G. Qian, and B. Chen, Methane storage in metal-organic frameworks, Chem. Soc. Rev, vol.43, pp.5657-5678, 2014.

N. C. Burtch, H. Jasuja, and K. S. Walton, Water stability and adsorption in metal-organic frameworks, Chem. Rev, vol.114, pp.10575-10612, 2014.

A. Zakutayev, N. Wunder, M. Schwarting, J. D. Perkins, R. White et al., An open experimental database for exploring inorganic materials. Sci. Data, vol.5, p.180053, 2018.

A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards et al., Commentary: The materials project: A materials genome approach to accelerating materials innovation, APL Materials, vol.1, p.11002, 2013.

R. Gaillac, P. Pullumbi, and F. X. Coudert, Elate: an open-source online application for analysis and visualization of elastic tensors, J. Phys. Condens. Matter, vol.28, p.275201, 2016.

M. De-jong, W. Chen, T. Angsten, A. Jain, R. Notestine et al., Charting the complete elastic properties of inorganic crystalline compounds, Sci. Data, vol.2, p.150009, 2015.

D. W. Davies, K. T. Butler, A. J. Jackson, A. Morris, J. M. Frost et al., Computational screening of all stoichiometric inorganic materials. Chem, vol.1, pp.617-627, 2016.

K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, Machine learning for molecular and materials science, Nature, vol.559, pp.547-555, 2018.

F. Brockherde, L. Vogt, L. Li, M. E. Tuckerman, K. Burke et al., Bypassing the kohn-sham equations with machine learning, Nature Commun, vol.8, p.1133, 2017.

J. Hollingsworth, T. E. Baker, and K. Burke, Can exact conditions improve machine-learned density functionals?, J. Chem. Phys, vol.148, p.241743, 2018.

O. Schütt and J. Vandevondele, Machine learning adaptive basis sets for efficient large scale density functional theory simulation, J. Chem. Theory Comput, vol.14, pp.4168-4175, 2018.

E. Kim, K. Huang, A. Saunders, A. Mccallum, G. Ceder et al., Materials synthesis insights from scientific literature via text extraction and machine learning, Chem. Mater, vol.29, pp.9436-9444, 2017.

R. Gómez-bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-lobato, B. Sánchez-lengeling et al., Automatic chemical design using a data-driven continuous representation of molecules, ACS Cent. Sci, vol.4, pp.268-276, 2018.

B. R. Goldsmith, J. Esterhuizen, J. X. Liu, C. J. Bartel, and C. Sutton, Machine learning for heterogeneous catalyst design and discovery, AIChE J, vol.64, pp.2311-2323, 2018.

M. De-jong, W. Chen, R. Notestine, K. Persson, G. Ceder et al., A statistical learning framework for materials science: Application to elastic moduli of k-nary inorganic polycrystalline compounds, Sci. Rep, vol.6, 2016.

J. D. Evans and F. X. Coudert, Predicting the mechanical properties of zeolite frameworks by machine learning, Chem. Mater, vol.29, pp.7833-7839, 2017.

F. X. Coudert, Systematic investigation of the mechanical properties of pure silica zeolites: stiffness, anisotropy, and negative linear compressibility, Phys. Chem. Chem. Phys, vol.15, p.16012, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02116883

R. Gaillac, Molecular modeling of physico-chemical properties in microporous solids, 2018.
URL : https://hal.archives-ouvertes.fr/tel-01820463

R. Gómez-bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-lobato, B. Sánchez-lengeling et al., Automatic chemical design using a data-driven continuous representation of molecules, ACS Cent. Sci, vol.4, pp.268-276, 2018.

A. Sturluson, M. T. Huynh, A. York, and C. M. Simon, Eigencages: Learning a latent space of porous cage molecules, ACS Cent. Sci, vol.4, pp.1663-1676, 2018.