Modelling of framework materials at multiple scales: current practices and open questions

The last decade has seen an explosion of the family of framework materials and their study, from both the experimental and computational points of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of material databases and the growing use of machine learning for the prediction of properties. This article is part of the theme issue ‘Mineralomimesis: natural and synthetic frameworks in science and technology’.

[1]  Harold A. Scheraga,et al.  A polarizable force field for water using an artificial neural network , 2002 .

[2]  François-Xavier Coudert,et al.  Defects and disorder in metal organic frameworks. , 2016, Dalton transactions.

[3]  Maciej Haranczyk,et al.  Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals , 2014 .

[4]  T. Bučko,et al.  Monomolecular cracking of propane over acidic chabazite: An ab initio molecular dynamics and transition path sampling study , 2011 .

[5]  Jihan Kim,et al.  Text Mining Metal-Organic Framework Papers , 2017, J. Chem. Inf. Model..

[6]  Ghosh,et al.  Density-functional theory for time-dependent systems. , 1987, Physical review. A, General physics.

[7]  Y. Yue,et al.  Metal-organic framework glasses with permanent accessible porosity , 2018, Nature Communications.

[8]  M. Zwijnenburg,et al.  Absence of Limitations on the Framework Density and Pore Size of High-Silica Zeolites , 2008 .

[9]  C. Wilmer,et al.  Large-scale screening of hypothetical metal-organic frameworks. , 2012, Nature chemistry.

[10]  R. Schmid,et al.  Computational Structure Prediction of (4,4)-Connected Copper Paddle-wheel-based MOFs: Influence of Ligand Functionalization on the Topological Preference , 2018 .

[11]  Yamil J. Colón,et al.  High-throughput computational screening of metal-organic frameworks. , 2014, Chemical Society reviews.

[12]  David S Sholl,et al.  Accelerating applications of metal-organic frameworks for gas adsorption and separation by computational screening of materials. , 2012, Langmuir : the ACS journal of surfaces and colloids.

[13]  Noam Bernstein,et al.  Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics. , 2018, The journal of physical chemistry letters.

[14]  Kristin A. Persson,et al.  Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .

[15]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[16]  Wei Chen,et al.  A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds , 2016, Scientific Reports.

[17]  Jihong Yu,et al.  FraGen: a computer program for real‐space structure solution of extended inorganic frameworks , 2012 .

[18]  Igor Rivin,et al.  Enumeration of periodic tetrahedral frameworks. II. Polynodal graphs , 2004 .

[19]  Yi Li,et al.  New stories of zeolite structures: their descriptions, determinations, predictions, and evaluations. , 2014, Chemical reviews.

[20]  Joost VandeVondele,et al.  Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation , 2018, Journal of chemical theory and computation.

[21]  J S Smith,et al.  ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.

[22]  A. McCallum,et al.  Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning , 2017 .

[23]  François-Xavier Coudert,et al.  Liquid metal-organic frameworks. , 2017, Nature materials.

[24]  François-Xavier Coudert,et al.  Unexpected coupling between flow and adsorption in porous media. , 2015, Soft matter.

[25]  François-Xavier Coudert,et al.  Systematic investigation of the mechanical properties of pure silica zeolites: stiffness, anisotropy, and negative linear compressibility. , 2013, Physical chemistry chemical physics : PCCP.

[26]  Romain Gaillac,et al.  ELATE: an open-source online application for analysis and visualization of elastic tensors , 2016, Journal of physics. Condensed matter : an Institute of Physics journal.

[27]  François-Xavier Coudert,et al.  Responsive Metal–Organic Frameworks and Framework Materials: Under Pressure, Taking the Heat, in the Spotlight, with Friends , 2015 .

[28]  Christopher J. Bartel,et al.  Machine learning for heterogeneous catalyst design and discovery , 2018 .

[29]  A. Goodwin,et al.  Compositional inhomogeneity and tuneable thermal expansion in mixed-metal ZIF-8 analogues. , 2018, Chemical communications.

[30]  Louis Vanduyfhuys,et al.  QuickFF: A program for a quick and easy derivation of force fields for metal‐organic frameworks from ab initio input , 2015, J. Comput. Chem..

[31]  T. Morawietz,et al.  A density-functional theory-based neural network potential for water clusters including van der Waals corrections. , 2013, The journal of physical chemistry. A.

[32]  V. Blatov,et al.  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 , 2013 .

[33]  Senja Barthel,et al.  Distinguishing Metal–Organic Frameworks , 2018, Crystal growth & design.

[34]  J. Coe,et al.  An efficient approach to ab initio Monte Carlo simulation. , 2013, The Journal of chemical physics.

[35]  S. Kitagawa,et al.  Soft porous crystals. , 2009, Nature chemistry.

[36]  D. Truhlar,et al.  Cerium Metal-Organic Framework for Photocatalysis. , 2018, Journal of the American Chemical Society.

[37]  M. Deem,et al.  A biased Monte Carlo scheme for zeolite structure solution , 1998, cond-mat/9809085.

[38]  Yi Li,et al.  In silico prediction and screening of modular crystal structures via a high-throughput genomic approach , 2015, Nature Communications.

[39]  I. Ciofini,et al.  Modelling photophysical properties of metal-organic frameworks: a density functional theory based approach. , 2016, Physical chemistry chemical physics : PCCP.

[40]  Robert G. Bell,et al.  Advances in Theory and Their Application within the Field of Zeolite Chemistry , 2015 .

[41]  Michael W. Deem,et al.  Toward a Database of Hypothetical Zeolite Structures , 2006 .

[42]  C. Angell,et al.  Nanoporous Transparent MOF Glasses with Accessible Internal Surface. , 2016, Journal of the American Chemical Society.

[43]  Michele Parrinello,et al.  Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.

[44]  Cory M. Simon,et al.  Eigencages: Learning a Latent Space of Porous Cage Molecules , 2018, ACS central science.

[45]  Michael Treacy,et al.  Enumeration of periodic tetrahedral frameworks , 1997 .

[46]  T. Nenoff,et al.  Radioactive iodine capture in silver-containing mordenites through nanoscale silver iodide formation. , 2010, Journal of the American Chemical Society.

[47]  Volker L. Deringer,et al.  Machine learning based interatomic potential for amorphous carbon , 2016, 1611.03277.

[48]  François-Xavier Coudert,et al.  Macroscopic Simulation of Deformation in Soft Microporous Composites. , 2017, The journal of physical chemistry letters.

[49]  Krista S. Walton,et al.  Water stability and adsorption in metal-organic frameworks. , 2014, Chemical reviews.

[50]  P. Senet,et al.  Combining the Monte Carlo Technique with 29SI NMR Spectroscopy: Simulations of Cation Locations in Zeolites with Various Si/Al Ratios , 2001 .

[51]  R. Crespo‐Otero,et al.  Modelling a Linker Mix‐and‐Match Approach for Controlling the Optical Excitation Gaps and Band Alignment of Zeolitic Imidazolate Frameworks , 2016, Angewandte Chemie.

[52]  François-Xavier Coudert,et al.  Encoding complexity within supramolecular analogues of frustrated magnets. , 2016, Nature chemistry.

[53]  Jihong Yu,et al.  Insight into the construction of open-framework aluminophosphates. , 2006, Chemical Society reviews.

[54]  A. Corma,et al.  Synthesis of New Zeolite Structures , 2015 .

[55]  Jeffrey S. Camp,et al.  A Comprehensive Set of High-Quality Point Charges for Simulations of Metal–Organic Frameworks , 2016 .

[56]  M. E. Casida Time-Dependent Density Functional Response Theory for Molecules , 1995 .

[57]  Jihong Yu,et al.  Rational approaches toward the design and synthesis of zeolitic inorganic open-framework materials. , 2010, Accounts of chemical research.

[58]  Krista S. Walton,et al.  Understanding Structure, Metal Distribution, and Water Adsorption in Mixed-Metal MOF-74 , 2017 .

[59]  G. Sastre,et al.  Screening of hypothetical metal-organic frameworks for H2 storage. , 2014, Physical chemistry chemical physics : PCCP.

[60]  Aron Walsh,et al.  Electronic Chemical Potentials of Porous Metal–Organic Frameworks , 2014, Journal of the American Chemical Society.

[61]  A. Fuchs,et al.  Computational characterization and prediction of metal-organic framework properties , 2015, 1506.08219.

[62]  Cormac Toher,et al.  Charting the complete elastic properties of inorganic crystalline compounds , 2015, Scientific Data.

[63]  K. Chapman,et al.  Exploiting high pressures to generate porosity, polymorphism, and lattice expansion in the nonporous molecular framework Zn(CN)2. , 2013, Journal of the American Chemical Society.

[64]  T. K. Roy,et al.  MOF‐FF – A flexible first‐principles derived force field for metal‐organic frameworks , 2013 .

[65]  Michael W Deem,et al.  A database of new zeolite-like materials. , 2011, Physical chemistry chemical physics : PCCP.

[66]  S. Agrestini,et al.  Spin correlations in Ca3Co2O6: Polarized-neutron diffraction and Monte Carlo study , 2013, 1312.5243.

[67]  M. Allendorf,et al.  Computational screening of metal-organic frameworks for large-molecule chemical sensing. , 2010, Physical chemistry chemical physics : PCCP.

[68]  Michael J. Cafarella,et al.  Theoretical Limits of Hydrogen Storage in Metal–Organic Frameworks: Opportunities and Trade-Offs , 2013 .

[69]  François-Xavier Coudert,et al.  A pressure-amplifying framework material with negative gas adsorption transitions , 2016, Nature.

[70]  W. Goddard,et al.  UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations , 1992 .

[71]  Melt-Quenched Glasses of Metal-Organic Frameworks. , 2016, Journal of the American Chemical Society.

[72]  Aron Walsh,et al.  Computational Screening of All Stoichiometric Inorganic Materials , 2016, Chem.

[73]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[74]  Hannes Jónsson,et al.  Silica glass structure generation for ab initio calculations using small samples of amorphous silica , 2005 .

[75]  F. Illas,et al.  Apparent scarcity of low-density polymorphs of inorganic solids. , 2010, Physical review letters.

[76]  A. Hill,et al.  Methane storage in metal organic frameworks , 2012 .

[77]  François-Xavier Coudert,et al.  Impacts of the Imidazolate Linker Substitution (CH3, Cl, or Br) on the Structural and Adsorptive Properties of ZIF-8 , 2018, The Journal of Physical Chemistry C.

[78]  A. Goodwin,et al.  Negative linear compressibility. , 2015, Physical chemistry chemical physics : PCCP.

[79]  Yi Li,et al.  Design of Zeolite Frameworks with Defined Pore Geometry through Constrained Assembly of Atoms , 2003 .

[80]  Louis Vanduyfhuys,et al.  Extension of the QuickFF force field protocol for an improved accuracy of structural, vibrational, mechanical and thermal properties of metal–organic frameworks , 2018, J. Comput. Chem..

[81]  T. Verstraelen,et al.  Ab Initio Parametrized Force Field for the Flexible Metal-Organic Framework MIL-53(Al). , 2012, Journal of chemical theory and computation.

[82]  Joost VandeVondele,et al.  Isobaric-isothermal monte carlo simulations from first principles: application to liquid water at ambient conditions. , 2004, Chemphyschem : a European journal of chemical physics and physical chemistry.

[83]  Kieron Burke,et al.  Can exact conditions improve machine-learned density functionals? , 2018, The Journal of chemical physics.

[84]  Li Li,et al.  Bypassing the Kohn-Sham equations with machine learning , 2016, Nature Communications.

[85]  Peyman Z. Moghadam,et al.  Development of a Cambridge Structural Database Subset: A Collection of Metal-Organic Frameworks for Past, Present, and Future , 2017 .

[86]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[87]  M. Zwijnenburg,et al.  An extensive theoretical survey of low-density allotropy in silicon. , 2010, Physical chemistry chemical physics : PCCP.

[88]  Jörg Behler,et al.  Neural Network Potentials in Materials Modeling , 2020, Handbook of Materials Modeling.

[89]  M. Hill,et al.  Dynamic photo-switching in metal-organic frameworks as a route to low-energy carbon dioxide capture and release. , 2013, Angewandte Chemie.

[90]  François-Xavier Coudert,et al.  Adsorption deformation of microporous composites. , 2016, Dalton transactions.

[91]  S. Agrestini,et al.  Spin correlations in Ca 3 Co 2 O 6 : Polarized-neutron diffraction and Monte Carlo study , 2014 .

[92]  François-Xavier Coudert,et al.  Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning , 2017 .

[93]  Mark A. Rodriguez,et al.  SILVER-MORDENITE FOR RADIOLOGIC GAS CAPTURE FROM COMPLEX STREAMS: DUAL CATALYTIC CH3I DECOMPOSITION AND I CONFINEMENT. , 2014 .

[94]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[95]  German Sastre,et al.  Feasibility of Pure Silica Zeolites , 2010 .

[96]  John D. Perkins,et al.  An open experimental database for exploring inorganic materials , 2018, Scientific Data.

[97]  W. Li,et al.  Pressure-induced bond rearrangement and reversible phase transformation in a metal-organic framework. , 2014, Angewandte Chemie.

[98]  R. Schmid,et al.  Coarse graining of force fields for metal-organic frameworks. , 2016, Dalton transactions.

[99]  L. Broadbelt,et al.  Computational screening of metal-organic frameworks for xenon/krypton separation , 2011 .