Predicting hydrogen storage in MOFs via machine learning

Ahmed, Alauddin; Siegel, Donald J.

doi:10.1016/j.patter.2021.100291

Predicting hydrogen storage in MOFs via machine learning

Journal Article · Thu Jul 01 00:00:00 EDT 2021 · Patterns

DOI:https://doi.org/10.1016/j.patter.2021.100291· OSTI ID:1798400

Ahmed, Alauddin;

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Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0007046

OSTI ID:: 1798400

Journal Information:: Patterns, Journal Name: Patterns Journal Issue: 7 Vol. 2; ISSN 2666-3899

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (106)

Data‐Driven Materials Science: Status, Challenges, and Perspectives Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart Advanced Science, Vol. 6, Issue 21 https://doi.org/10.1002/advs.201900808	journal	September 2019
Terminologie von Metall-organischen Gerüstverbindungen und Koordinationspolymeren (IUPAC-Empfehlungen 2013) Fischer, Roland A.; Schwedler, Inke Angewandte Chemie, Vol. 126, Issue 27 https://doi.org/10.1002/ange.201400619	journal	May 2014
An Isoreticular Series of Metal-Organic Frameworks with Dendritic Hexacarboxylate Ligands and Exceptionally High Gas-Uptake Capacity Yuan, Daqiang; Zhao, Dan; Sun, Daofeng Angewandte Chemie International Edition, Vol. 49, Issue 31, p. 5357-5361 https://doi.org/10.1002/anie.201001009	journal	June 2010
Preferred Hydrogen Adsorption Sites in Various MOFs-A Comparative Computational Study Fischer, Michael; Hoffmann, Frank; Fröba, Michael ChemPhysChem, Vol. 10, Issue 15 https://doi.org/10.1002/cphc.200900459	journal	October 2009
Quantitative Structure-Property Relationship Models for Recognizing Metal Organic Frameworks (MOFs) with High CO ₂ Working Capacity and CO ₂ /CH ₄ Selectivity for Methane Purification : Quantitative Structure-Property Relationship Models for Recognizing Metal Organic Frameworks (MOFs) with High CO2 Working Capacity and CO2/CH4 Selecti Aghaji, Mohammad Zein; Fernandez, Michael; Boyd, Peter G. European Journal of Inorganic Chemistry, Vol. 2016, Issue 27 https://doi.org/10.1002/ejic.201600365	journal	June 2016
Machine learning in materials science Wei, Jing; Chu, Xuan; Sun, Xiang‐Yu InfoMat, Vol. 1, Issue 3 https://doi.org/10.1002/inf2.12028	journal	August 2019
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting Freund, Yoav; Schapire, Robert E. Journal of Computer and System Sciences, Vol. 55, Issue 1 https://doi.org/10.1006/jcss.1997.1504	journal	August 1997
Bagging predictors Breiman, Leo Machine Learning, Vol. 24, Issue 2, p. 123-140 https://doi.org/10.1007/BF00058655	journal	August 1996
Outlook and challenges for hydrogen storage in nanoporous materials Broom, D. P.; Webb, C. J.; Hurst, K. E. Applied Physics A, Vol. 122, Issue 3 https://doi.org/10.1007/s00339-016-9651-4	journal	February 2016
Gas adsorption process in activated carbon over a wide temperature range above the critical point. Part 1: modified Dubinin-Astakhov model Richard, M. -A.; Bénard, P.; Chahine, R. Adsorption, Vol. 15, Issue 1 https://doi.org/10.1007/s10450-009-9149-x	journal	February 2009
Extremely randomized trees Geurts, Pierre; Ernst, Damien; Wehenkel, Louis Machine Learning, Vol. 63, Issue 1 https://doi.org/10.1007/s10994-006-6226-1	journal	March 2006
Investigation of the hydrogen adsorbed density inside the pores of MOF-5 from path integral grand canonical Monte Carlo at supercritical and subcritical temperature Durette, David; Bénard, Pierre; Zacharia, Renju Science Bulletin, Vol. 61, Issue 8 https://doi.org/10.1007/s11434-016-1027-9	journal	April 2016
Hydrogen adsorption in different carbon nanostructures Panella, Barbara; Hirscher, Michael; Roth, Siegmar Carbon, Vol. 43, Issue 10, p. 2209-2214 https://doi.org/10.1016/j.carbon.2005.03.037	journal	August 2005
The U.S. Department of Energy's National Hydrogen Storage Project: Progress towards meeting hydrogen-powered vehicle requirements Satyapal, Sunita; Petrovic, John; Read, Carole Catalysis Today, Vol. 120, Issue 3-4 https://doi.org/10.1016/j.cattod.2006.09.022	journal	February 2007
Estimation of system-level hydrogen storage for metal-organic frameworks with high volumetric storage density Purewal, Justin; Veenstra, Mike; Tamburello, David International Journal of Hydrogen Energy, Vol. 44, Issue 29 https://doi.org/10.1016/j.ijhydene.2019.04.082	journal	June 2019
Characterization and comparison of pore landscapes in crystalline porous materials Pinheiro, Marielle; Martin, Richard L.; Rycroft, Chris H. Journal of Molecular Graphics and Modelling, Vol. 44 https://doi.org/10.1016/j.jmgm.2013.05.007	journal	July 2013
Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials Willems, Thomas F.; Rycroft, Chris H.; Kazi, Michaeel Microporous and Mesoporous Materials, Vol. 149, Issue 1 https://doi.org/10.1016/j.micromeso.2011.08.020	journal	February 2012
An extensive experimental survey of regression methods Fernández-Delgado, M.; Sirsat, M. S.; Cernadas, E. Neural Networks, Vol. 111 https://doi.org/10.1016/j.neunet.2018.12.010	journal	March 2019
Topologically Guided, Automated Construction of Metal–Organic Frameworks and Their Evaluation for Energy-Related Applications Colón, Yamil J.; Gómez-Gualdrón, Diego A.; Snurr, Randall Q. Crystal Growth & Design, Vol. 17, Issue 11 https://doi.org/10.1021/acs.cgd.7b00848	journal	September 2017
Distinguishing Metal–Organic Frameworks Barthel, Senja; Alexandrov, Eugeny V.; Proserpio, Davide M. Crystal Growth & Design, Vol. 18, Issue 3 https://doi.org/10.1021/acs.cgd.7b01663	journal	January 2018
Materials Informatics with PoreBlazer v4.0 and the CSD MOF Database Sarkisov, Lev; Bueno-Perez, Rocio; Sutharson, Mythili Chemistry of Materials, Vol. 32, Issue 23 https://doi.org/10.1021/acs.chemmater.0c03575	journal	November 2020
Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage Thornton, Aaron W.; Simon, Cory M.; Kim, Jihan Chemistry of Materials, Vol. 29, Issue 7 https://doi.org/10.1021/acs.chemmater.6b04933	journal	March 2017
Development of a Cambridge Structural Database Subset: A Collection of Metal–Organic Frameworks for Past, Present, and Future Moghadam, Peyman Z.; Li, Aurelia; Wiggin, Seth B. Chemistry of Materials, Vol. 29, Issue 7 https://doi.org/10.1021/acs.chemmater.7b00441	journal	March 2017
Role of Pore Chemistry and Topology in the CO ₂ Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning Anderson, Ryther; Rodgers, Jacob; Argueta, Edwin Chemistry of Materials, Vol. 30, Issue 18 https://doi.org/10.1021/acs.chemmater.8b02257	journal	August 2018
Mixed-Metal Strategy on Metal–Organic Frameworks (MOFs) for Functionalities Expansion: Co Substitution Induces Aerobic Oxidation of Cyclohexene over Inactive Ni-MOF-74 Sun, Dengrong; Sun, Fangxiang; Deng, Xiaoyu Inorganic Chemistry, Vol. 54, Issue 17 https://doi.org/10.1021/acs.inorgchem.5b01278	journal	August 2015
Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal–Organic Framework Database: CoRE MOF 2019 Chung, Yongchul G.; Haldoupis, Emmanuel; Bucior, Benjamin J. Journal of Chemical & Engineering Data, Vol. 64, Issue 12 https://doi.org/10.1021/acs.jced.9b00835	journal	November 2019
Mapping of Drug-like Chemical Universe with Reduced Complexity Molecular Frameworks Kontijevskis, Aleksejs Journal of Chemical Information and Modeling, Vol. 57, Issue 4 https://doi.org/10.1021/acs.jcim.7b00006	journal	April 2017
A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials Fanourgakis, George S.; Gkagkas, Konstantinos; Tylianakis, Emmanuel The Journal of Physical Chemistry A, Vol. 123, Issue 28 https://doi.org/10.1021/acs.jpca.9b03290	journal	June 2019
Critical Factors in Computational Characterization of Hydrogen Storage in Metal–Organic Frameworks Camp, Jeffrey; Stavila, Vitalie; Allendorf, Mark D. The Journal of Physical Chemistry C, Vol. 122, Issue 33 https://doi.org/10.1021/acs.jpcc.8b04021	journal	July 2018
Attainable Volumetric Targets for Adsorption-Based Hydrogen Storage in Porous Crystals: Molecular Simulation and Machine Learning Anderson, Grace; Schweitzer, Benjamin; Anderson, Ryther The Journal of Physical Chemistry C, Vol. 123, Issue 1 https://doi.org/10.1021/acs.jpcc.8b09420	journal	December 2018
Force-Field Prediction of Materials Properties in Metal-Organic Frameworks Boyd, Peter G.; Moosavi, Seyed Mohamad; Witman, Matthew The Journal of Physical Chemistry Letters, Vol. 8, Issue 2 https://doi.org/10.1021/acs.jpclett.6b02532	journal	January 2017
High-Throughput Computational Screening of Multivariate Metal–Organic Frameworks (MTV-MOFs) for CO ₂ Capture Li, Song; Chung, Yongchul G.; Simon, Cory M. The Journal of Physical Chemistry Letters, Vol. 8, Issue 24 https://doi.org/10.1021/acs.jpclett.7b02700	journal	December 2017
Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning Witman, Matthew; Ling, Sanliang; Grant, David M. The Journal of Physical Chemistry Letters, Vol. 11, Issue 1 https://doi.org/10.1021/acs.jpclett.9b02971	journal	December 2019
Accurate Characterization of the Pore Volume in Microporous Crystalline Materials Ongari, Daniele; Boyd, Peter G.; Barthel, Senja Langmuir, Vol. 33, Issue 51 https://doi.org/10.1021/acs.langmuir.7b01682	journal	July 2017
Understanding Volumetric and Gravimetric Hydrogen Adsorption Trade-off in Metal–Organic Frameworks Gómez-Gualdrón, Diego A.; Wang, Timothy C.; García-Holley, Paula ACS Applied Materials & Interfaces, Vol. 9, Issue 39 https://doi.org/10.1021/acsami.7b01190	journal	April 2017
Geometrical Properties Can Predict CO ₂ and N ₂ Adsorption Performance of Metal–Organic Frameworks (MOFs) at Low Pressure Fernandez, Michael; Barnard, Amanda S. ACS Combinatorial Science, Vol. 18, Issue 5 https://doi.org/10.1021/acscombsci.5b00188	journal	April 2016
Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs) Pardakhti, Maryam; Moharreri, Ehsan; Wanik, David ACS Combinatorial Science, Vol. 19, Issue 10 https://doi.org/10.1021/acscombsci.7b00056	journal	September 2017
Benchmark Study of Hydrogen Storage in Metal–Organic Frameworks under Temperature and Pressure Swing Conditions García-Holley, Paula; Schweitzer, Benjamin; Islamoglu, Timur ACS Energy Letters, Vol. 3, Issue 3 https://doi.org/10.1021/acsenergylett.8b00154	journal	February 2018
The Chemical Space Project Reymond, Jean-Louis Accounts of Chemical Research, Vol. 48, Issue 3 https://doi.org/10.1021/ar500432k	journal	February 2015
Addressing Challenges of Identifying Geometrically Diverse Sets of Crystalline Porous Materials Martin, Richard Luis; Smit, Berend; Haranczyk, Maciej Journal of Chemical Information and Modeling, Vol. 52, Issue 2 https://doi.org/10.1021/ci200386x	journal	December 2011
Opening Metal–Organic Frameworks Vol. 2: Inserting Longer Pillars into Pillared-Paddlewheel Structures through Solvent-Assisted Linker Exchange Karagiaridi, Olga; Bury, Wojciech; Tylianakis, Emmanuel Chemistry of Materials, Vol. 25, Issue 17 https://doi.org/10.1021/cm401724v	journal	August 2013
Theoretical Limits of Hydrogen Storage in Metal–Organic Frameworks: Opportunities and Trade-Offs Goldsmith, Jacob; Wong-Foy, Antek G.; Cafarella, Michael J. Chemistry of Materials, Vol. 25, Issue 16 https://doi.org/10.1021/cm401978e	journal	August 2013
Computational Design of Metal–Organic Frameworks Based on Stable Zirconium Building Units for Storage and Delivery of Methane Gomez-Gualdron, Diego A.; Gutov, Oleksii V.; Krungleviciute, Vaiva Chemistry of Materials, Vol. 26, Issue 19 https://doi.org/10.1021/cm502304e	journal	September 2014
Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals Chung, Yongchul G.; Camp, Jeffrey; Haranczyk, Maciej Chemistry of Materials, Vol. 26, Issue 21 https://doi.org/10.1021/cm502594j	journal	October 2014
A Highly Porous Interpenetrated Metal–Organic Framework from the Use of a Novel Nanosized Organic Linker Manos, Manolis J.; Markoulides, Marios S.; Malliakas, Christos D. Inorganic Chemistry, Vol. 50, Issue 22 https://doi.org/10.1021/ic201919q	journal	November 2011
Exceptional H₂ Saturation Uptake in Microporous Metal−Organic Frameworks Wong-Foy, Antek G.; Matzger, Adam J.; Yaghi, Omar M. Journal of the American Chemical Society, Vol. 128, Issue 11, p. 3494-3495 https://doi.org/10.1021/ja058213h	journal	March 2006
Metal−Organic Polyhedral Frameworks: High H ₂ Adsorption Capacities and Neutron Powder Diffraction Studies Yan, Yong; Telepeni, Irvin; Yang, Sihai Journal of the American Chemical Society, Vol. 132, Issue 12 https://doi.org/10.1021/ja1001407	journal	March 2010
In silico Design of Porous Polymer Networks: High-Throughput Screening for Methane Storage Materials Martin, Richard L.; Simon, Cory M.; Smit, Berend Journal of the American Chemical Society, Vol. 136, Issue 13 https://doi.org/10.1021/ja4123939	journal	March 2014
Application of Consistency Criteria To Calculate BET Areas of Micro- And Mesoporous Metal–Organic Frameworks Gómez-Gualdrón, Diego A.; Moghadam, Peyman Z.; Hupp, Joseph T. Journal of the American Chemical Society, Vol. 138, Issue 1 https://doi.org/10.1021/jacs.5b10266	journal	December 2015
Large-Scale Quantitative Structure–Property Relationship (QSPR) Analysis of Methane Storage in Metal–Organic Frameworks Fernandez, Michael; Woo, Tom K.; Wilmer, Christopher E. The Journal of Physical Chemistry C, Vol. 117, Issue 15 https://doi.org/10.1021/jp4006422	journal	April 2013
Mail-Order Metal–Organic Frameworks (MOFs): Designing Isoreticular MOF-5 Analogues Comprising Commercially Available Organic Molecules Martin, Richard L.; Lin, Li-Chiang; Jariwala, Kuldeep The Journal of Physical Chemistry C, Vol. 117, Issue 23 https://doi.org/10.1021/jp401920y	journal	April 2013
Atomic Property Weighted Radial Distribution Functions Descriptors of Metal–Organic Frameworks for the Prediction of Gas Uptake Capacity Fernandez, Michael; Trefiak, Nicholas R.; Woo, Tom K. The Journal of Physical Chemistry C, Vol. 117, Issue 27 https://doi.org/10.1021/jp404287t	journal	July 2013
AuToGraFS: Automatic Topological Generator for Framework Structures Addicoat, Matthew A.; Coupry, Damien E.; Heine, Thomas The Journal of Physical Chemistry A, Vol. 118, Issue 40 https://doi.org/10.1021/jp507643v	journal	September 2014
In Silico Discovery of High Deliverable Capacity Metal–Organic Frameworks Bao, Yi; Martin, Richard L.; Simon, Cory M. The Journal of Physical Chemistry C, Vol. 119, Issue 1 https://doi.org/10.1021/jp5123486	journal	December 2014
Tuning Metal–Organic Frameworks with Open-Metal Sites and Its Origin for Enhancing CO ₂ Affinity by Metal Substitution Park, Joonho; Kim, Heejin; Han, Sang Soo The Journal of Physical Chemistry Letters, Vol. 3, Issue 7 https://doi.org/10.1021/jz300047n	journal	March 2012
Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO ₂ Capture Fernandez, Michael; Boyd, Peter G.; Daff, Thomas D. The Journal of Physical Chemistry Letters, Vol. 5, Issue 17 https://doi.org/10.1021/jz501331m	journal	August 2014
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
A tutorial on support vector regression Smola, Alex J.; Schölkopf, Bernhard Statistics and Computing, Vol. 14, Issue 3 https://doi.org/10.1023/B:STCO.0000035301.49549.88	journal	August 2004
Large-scale screening of hypothetical metal–organic frameworks Wilmer, Christopher E.; Leaf, Michael; Lee, Chang Yeon Nature Chemistry, Vol. 4, Issue 2, p. 83-89 https://doi.org/10.1038/nchem.1192	journal	November 2011
Capturing chemical intuition in synthesis of metal-organic frameworks Moosavi, Seyed Mohamad; Chidambaram, Arunraj; Talirz, Leopold Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-08483-9	journal	February 2019
Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks Ahmed, Alauddin; Seth, Saona; Purewal, Justin Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-09365-w	journal	April 2019
Machine learning for molecular and materials science Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh Nature, Vol. 559, Issue 7715 https://doi.org/10.1038/s41586-018-0337-2	journal	July 2018
Data-driven design of metal–organic frameworks for wet flue gas CO2 capture Boyd, Peter G.; Chidambaram, Arunraj; García-Díez, Enrique Nature, Vol. 576, Issue 7786 https://doi.org/10.1038/s41586-019-1798-7	journal	December 2019
Inverse design of nanoporous crystalline reticular materials with deep generative models Yao, Zhenpeng; Sánchez-Lengeling, Benjamín; Bobbitt, N. Scott Nature Machine Intelligence, Vol. 3, Issue 1 https://doi.org/10.1038/s42256-020-00271-1	journal	January 2021
High capacity hydrogenstorage materials: attributes for automotive applications and techniques for materials discovery Yang, Jun; Sudik, Andrea; Wolverton, Christopher Chem. Soc. Rev., Vol. 39, Issue 2 https://doi.org/10.1039/B802882F	journal	January 2010
In silico prediction of MOFs with high deliverable capacity or internal surface area Bao, Yi; Martin, Richard L.; Haranczyk, Maciej Physical Chemistry Chemical Physics, Vol. 17, Issue 18 https://doi.org/10.1039/C5CP00002E	journal	January 2015
A generalized method for constructing hypothetical nanoporous materials of any net topology from graph theory Boyd, Peter G.; Woo, Tom K. CrystEngComm, Vol. 18, Issue 21 https://doi.org/10.1039/C6CE00407E	journal	January 2016
Evaluating topologically diverse metal–organic frameworks for cryo-adsorbed hydrogen storage Gómez-Gualdrón, Diego A.; Colón, Yamil J.; Zhang, Xu Energy & Environmental Science, Vol. 9, Issue 10 https://doi.org/10.1039/C6EE02104B	journal	January 2016
In silico design and screening of hypothetical MOF-74 analogs and their experimental synthesis Witman, Matthew; Ling, Sanliang; Anderson, Samantha Chemical Science, Vol. 7, Issue 9 https://doi.org/10.1039/C6SC01477A	journal	January 2016
Balancing gravimetric and volumetric hydrogen density in MOFs Ahmed, Alauddin; Liu, Yiyang; Purewal, Justin Energy & Environmental Science, Vol. 10, Issue 11 https://doi.org/10.1039/C7EE02477K	journal	January 2017
Increasing topological diversity during computational “synthesis” of porous crystals: how and why Anderson, Ryther; Gómez-Gualdrón, Diego A. CrystEngComm, Vol. 21, Issue 10 https://doi.org/10.1039/C8CE01637B	journal	January 2019
An assessment of strategies for the development of solid-state adsorbents for vehicular hydrogen storage Allendorf, Mark D.; Hulvey, Zeric; Gennett, Thomas Energy & Environmental Science, Vol. 11, Issue 10 https://doi.org/10.1039/C8EE01085D	journal	January 2018
Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks Bucior, Benjamin J.; Bobbitt, N. Scott; Islamoglu, Timur Molecular Systems Design & Engineering, Vol. 4, Issue 1 https://doi.org/10.1039/C8ME00050F	journal	January 2019
A collection of forcefield precursors for metal–organic frameworks Chen, Taoyi; Manz, Thomas A. RSC Advances, Vol. 9, Issue 63 https://doi.org/10.1039/C9RA07327B	journal	January 2019
An extensive comparative analysis of two MOF databases: high-throughput screening of computation-ready MOFs for CH ₄ and H ₂ adsorption Altintas, Cigdem; Avci, Gokay; Daglar, Hilal Journal of Materials Chemistry A, Vol. 7, Issue 16 https://doi.org/10.1039/C9TA01378D	journal	January 2019
Large-scale computational assembly of ionic liquid/MOF composites: synergistic effect in the wire-tube conformation for efficient CO ₂ /CH ₄ separation Lan, Youshi; Yan, Tongan; Tong, Minman Journal of Materials Chemistry A, Vol. 7, Issue 20 https://doi.org/10.1039/C9TA01752F	journal	January 2019
Identifying misbonded atoms in the 2019 CoRE metal–organic framework database Chen, Taoyi; Manz, Thomas A. RSC Advances, Vol. 10, Issue 45 https://doi.org/10.1039/D0RA02498H	journal	January 2020
Independent verification of the saturation hydrogen uptake in MOF-177 and establishment of a benchmark for hydrogen adsorption in metal–organic frameworks Furukawa, Hiroyasu; Miller, Michael A.; Yaghi, Omar M. Journal of Materials Chemistry, Vol. 17, Issue 30, p. 3197-3204 https://doi.org/10.1039/b703608f	journal	January 2007
Luminescent metal–organic frameworks Allendorf, M. D.; Bauer, C. A.; Bhakta, R. K. Chemical Society Reviews, Vol. 38, Issue 5 https://doi.org/10.1039/b802352m	journal	January 2009
Using molecular simulation to characterise metal–organic frameworks for adsorption applications Düren, Tina; Bae, Youn-Sang; Snurr, Randall Q. Chemical Society Reviews, Vol. 38, Issue 5, p. 1237-1247 https://doi.org/10.1039/b803498m	journal	January 2009
Secondary building units, nets and bonding in the chemistry of metal–organic frameworks Tranchemontagne, David J.; Mendoza-Cortés, José L.; O’Keeffe, Michael Chemical Society Reviews, Vol. 38, Issue 5, p. 1257-1283 https://doi.org/10.1039/b817735j	journal	January 2009
A multifaceted approach to hydrogen storage Churchard, Andrew J.; Banach, Ewa; Borgschulte, Andreas Physical Chemistry Chemical Physics, Vol. 13, Issue 38 https://doi.org/10.1039/c1cp22312g	journal	January 2011
Coordination polymers, metal–organic frameworks and the need for terminology guidelines Batten, Stuart R.; Champness, Neil R.; Chen, Xiao-Ming CrystEngComm, Vol. 14, Issue 9 https://doi.org/10.1039/c2ce06488j	journal	January 2012
High accuracy geometric analysis of crystalline porous materials Pinheiro, Marielle; Martin, Richard L.; Rycroft, Chris H. CrystEngComm, Vol. 15, Issue 37 https://doi.org/10.1039/c3ce41057a	journal	January 2013
A microporous Zn(ii)–MOF with open metal sites: structure and selective adsorption properties Zheng, Xiaofang; Huang, Yumei; Duan, Jingui Dalton Transactions, Vol. 43, Issue 22 https://doi.org/10.1039/c4dt00307a	journal	January 2014
Hydrogen adsorption in metal-organic frameworks: The role of nuclear quantum effects Wahiduzzaman, Mohammad; Walther, Christian F. J.; Heine, Thomas The Journal of Chemical Physics, Vol. 141, Issue 6 https://doi.org/10.1063/1.4892670	journal	August 2014
Nets, tiles, and metal-organic frameworks O’Keeffe, Michael APL Materials, Vol. 2, Issue 12 https://doi.org/10.1063/1.4901292	journal	December 2014
On the inner workings of Monte Carlo codes Dubbeldam, David; Torres-Knoop, Ariana; Walton, Krista S. Molecular Simulation, Vol. 39, Issue 14-15 https://doi.org/10.1080/08927022.2013.819102	journal	December 2013
Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage Bobbitt, N. Scott; Snurr, Randall Q. Molecular Simulation, Vol. 45, Issue 14-15 https://doi.org/10.1080/08927022.2019.1597271	journal	April 2019
The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation Sturluson, Arni; Huynh, Melanie T.; Kaija, Alec R. Molecular Simulation, Vol. 45, Issue 14-15 https://doi.org/10.1080/08927022.2019.1648809	journal	August 2019
Mercury 4.0 : from visualization to analysis, design and prediction Macrae, Clare F.; Sovago, Ioana; Cottrell, Simon J. Journal of Applied Crystallography, Vol. 53, Issue 1 https://doi.org/10.1107/S1600576719014092	journal	February 2020
The Cambridge Structural Database Groom, Colin R.; Bruno, Ian J.; Lightfoot, Matthew P. Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials, Vol. 72, Issue 2, p. 171-179 https://doi.org/10.1107/S2052520616003954	journal	April 2016
No free lunch theorems for optimization Wolpert, D. H.; Macready, W. G. IEEE Transactions on Evolutionary Computation, Vol. 1, Issue 1 https://doi.org/10.1109/4235.585893	journal	April 1997
Python for Scientific Computing Oliphant, Travis E. Computing in Science & Engineering, Vol. 9, Issue 3 https://doi.org/10.1109/MCSE.2007.58	journal	January 2007
Python for Scientists and Engineers Millman, K. Jarrod; Aivazis, Michael Computing in Science & Engineering, Vol. 13, Issue 2 https://doi.org/10.1109/MCSE.2011.36	journal	March 2011
In silico discovery of metal-organic frameworks for precombustion CO ₂ capture using a genetic algorithm Chung, Yongchul G.; Gómez-Gualdrón, Diego A.; Li, Peng Science Advances, Vol. 2, Issue 10 https://doi.org/10.1126/sciadv.1600909	journal	October 2016
Multiple Functional Groups of Varying Ratios in Metal-Organic Frameworks Deng, H.; Doonan, C. J.; Furukawa, H. Science, Vol. 327, Issue 5967, p. 846-850 https://doi.org/10.1126/science.1181761	journal	February 2010
Ultrahigh Porosity in Metal-Organic Frameworks Furukawa, H.; Ko, N.; Go, Y. B. Science, Vol. 329, Issue 5990, p. 424-428 https://doi.org/10.1126/science.1192160	journal	July 2010
Balancing volumetric and gravimetric uptake in highly porous materials for clean energy Chen, Zhijie; Li, Penghao; Anderson, Ryther Science, Vol. 368, Issue 6488 https://doi.org/10.1126/science.aaz8881	journal	April 2020
LIBSVM: A library for support vector machines Chang, Chih-Chung; Lin, Chih-Jen ACM Transactions on Intelligent Systems and Technology, Vol. 2, Issue 3 https://doi.org/10.1145/1961189.1961199	journal	April 2011
machine. Friedman, Jerome H. The Annals of Statistics, Vol. 29, Issue 5 https://doi.org/10.1214/aos/1013203451	journal	October 2001
Terminology of metal–organic frameworks and coordination polymers (IUPAC Recommendations 2013) Batten, Stuart R.; Champness, Neil R.; Chen, Xiao-Ming Pure and Applied Chemistry, Vol. 85, Issue 8 https://doi.org/10.1351/PAC-REC-12-11-20	journal	July 2013
Physisorption of gases, with special reference to the evaluation of surface area and pore size distribution (IUPAC Technical Report) Thommes, Matthias; Kaneko, Katsumi; Neimark, Alexander V. Pure and Applied Chemistry, Vol. 87, Issue 9-10 https://doi.org/10.1515/pac-2014-1117	journal	October 2015
Hydrogen Fuel Cell Vehicles; Current Status and Future Prospect Manoharan, Yogesh; Hosseini, Seyed Ehsan; Butler, Brayden Applied Sciences, Vol. 9, Issue 11 https://doi.org/10.3390/app9112296	journal	June 2019
Let’s Talk about MOFs—Topology and Terminology of Metal-Organic Frameworks and Why We Need Them Öhrström, Lars Crystals, Vol. 5, Issue 1 https://doi.org/10.3390/cryst5010154	journal	February 2015
Expert Opinion Analysis on Renewable Hydrogen Storage Systems Potential in Europe Astiaso Garcia, Davide; Barbanera, Federica; Cumo, Fabrizio Energies, Vol. 9, Issue 11 https://doi.org/10.3390/en9110963	journal	November 2016

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