BetaCitation Atlas

RESEARCH PAPER · Chemistry of Materials · 2017

Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage

Aaron W. Thornton, Cory M. Simon, Jihan Kim, Ohmin Kwon, Kathryn S. Deeg, Kristina Konstas, Steven J. Pas, Matthew R. Hill, David Alan Winkler, Maciej Harańczyk, Berend Smit

Read original paper ↗

See where this research leads.

251 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen StorageOriginalDiscovery and Optimization of Materials Using Evolutionary Approaches204Computational Modeling and Simulation of CO2Capture by Aqueous Amines229Biomimetic molecular design tools that learn, evolve, and adapt12Computational development of the nanoporous materials genome180Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning202Topologically Guided, Automated Construction of Metal–Organic Frameworks and Their Evaluation for Energy-Related Applications320DFT simulation of hydrogen storage on manganese phosphorous trisulphide (MnPS3)20Materials genomics‐guided ab initio screening of MOFs with open copper sites for acetylene storage16Materials genome: research progress, challenges and outlook18Text Mining Metal–Organic Framework Papers68An assessment of strategies for the development of solid-state adsorbents for vehicular hydrogen storage311Hydrogen absorption in carbon allotropes and talc3High-Throughput Computational Screening of the Metal Organic Framework Database for CH4/H2 Separations146Effects of Force Field Selection on the Computational Ranking of MOFs for CO2 Separations51Sparse QSAR modelling methods for therapeutic and regenerative medicine19Enhanced $\text {CO}_2$ CO 2 selectivity within the cavity of gmelinite frameworks4Toward Effective Utilization of Methane: Machine Learning Prediction of Adsorption Energies on Metal Alloys182Computer-aided discovery of a metal–organic framework with superior oxygen uptake172Present and future of MOF research in the field of adsorption and molecular separation292Modeling Amorphous Microporous Polymers for CO2Capture and Separations272Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning0Dynamic and Static Mechanical Properties of Crosslinked Polymer Matrices: Multiscale Simulations and Experiments25In Silico Screening of Metal–Organic Frameworks for Adsorption-Driven Heat Pumps and Chillers48Current status and development trend of wind power generation-based hydrogen production technology89Critical Factors in Computational Characterization of Hydrogen Storage in Metal–Organic Frameworks32A new approach for the prediction of partition functions using machine learning techniques25Role of Pore Chemistry and Topology in the CO 2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning239Role of simple descriptors and applicability domain in predicting change in protein thermostability15High-Throughput Screening of Metal–Organic Frameworks for Macroscale Heteroepitaxial Alignment23Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks304Attainable Volumetric Targets for Adsorption-Based Hydrogen Storage in Porous Crystals: Molecular Simulation and Machine Learning101Toward Automated Tools for Characterization of Molecular Porosity8Computational Tuning of the Paddlewheel tcb-MOF Family for Advanced Methane Sorption5Increasing topological diversity during computational “synthesis” of porous crystals: how and why117Educational Modules on the Power-to-Gas Concept Demonstrate a Path to Renewable Energy Futures9Metal‐organic Framework Sponges4Screening Diffusion of Small Molecules in Flexible Zeolitic Imidazolate Frameworks Using a DFT-Parameterized Force Field56Understanding Quantitative Relationship between Methane Storage Capacities and Characteristic Properties of Metal–Organic Frameworks Based on Machine Learning75Data mining new energy materials from structure databases57The Role of Molecular Modeling & Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation0Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage115Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks489Structure-Mechanical Stability Relations of Metal-Organic Frameworks via Machine Learning277Application of data science tools to determine feature correlation and cluster metal hydrides for hydrogen storage20How to Design Hydrogen Storage Materials? Fundamentals, Synthesis, and Storage Tanks192Exploiting hydrophobicity and hydrophilicity in nanopores as a design principle for “smart” MOF microtanks for methane storage5The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation123Can the NIST Adsorption Database Be Used to Highlight Potential Materials for Gas Separation?0Can the NIST Adsorption Database Be Used to Highlight Potential Materials for Gas Separation?0Prediction of hydrogen adsorption in nanoporous materials from the energy distribution of adsorption sites36Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick4Autonomous Discovery in the Chemical Sciences Part I: Progress317Autonomous Discovery in the Chemical Sciences Part II: Outlook245Toward Interpretable Machine Learning Models for Materials Discovery42Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials123Machine Learning Prediction on Properties of Nanoporous Materials Utilizing Pore Geometry Barcodes47Machine Learning Enabled Tailor-Made Design of Application-Specific Metal–Organic Frameworks78Predicting Thermal Properties of Crystals Using Machine Learning58A Strong Test of Atomically Detailed Models of Molecular Adsorption in Zeolites Using Multilaboratory Experimental Data for CO2 Adsorption in Ammonium ZSM-529In Silico Investigation into H2 Uptake in MOFs: Combined Text/Data Mining and Structural Calculations11Screening PIM-1 performance as a membrane for binary mixture separation of gaseous organic compounds22Ensemble Learning of Partition Functions for the Prediction of Thermodynamic Properties of Adsorption in Metal–Organic and Covalent Organic Frameworks27An easy way to identify high performing covalent organic frameworks for hydrogen storage23Chemical looping hydrogen storage and production: use of binary ferrite-spinel as oxygen carrier materials20Design principles for the ultimate gas deliverable capacity material: nonporous to porous deformations without volume change8Few-atom cluster model systems for a hydrogen economy13Machine Learning-Guided Equations for Super-Fast Prediction of Methane Storage Capacities of COFs0Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks158On-chip readout plasmonic mid-IR gas sensor63Predicting Hydrogen Storage in Mofs via Machine Learning1Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model139Data Mining for Binary Separation Materials in Published Adsorption Isotherms28Selecting Adsorbents to Separate Diverse Near-Azeotropic Chemicals33Microstructure and formation mechanism of WC coating on tungsten fabricated by interstitial carburization: A multiscale investigation17The Chemistry of Reticular Framework Nanoparticles: MOF, ZIF, and COF Materials350Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning55Hollandites’ crystal chemistry, properties, and processing: a review27Revealing enhancement mechanism of volumetric hydrogen storage capacity of nano-porous frameworks by molecular simulation17Recent advances in knowledge discovery for heterogeneous catalysis using machine learning130Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Adsorption and Diffusion of Carbon Dioxide, Methane, and Their Mixture in Carbon Nanotubes in the Presence of Water61Artificial Intelligence Applied to the Prediction of Organic Materials12Machine Learning at the (Nano)materials-biology Interface4Applications of machine learning in metal-organic frameworks225Fast Screening of Large Databases for Top Performing Nanomaterials Using a Self-Consistent, Machine Learning Based Approach34Zn3V3O8 nanostructures: Facile hydrothermal/solvothermal synthesis, characterization, and electrochemical hydrogen storage127A GPU implementation of classical density functional theory for rapid prediction of gas adsorption in nanoporous materials20Ultrathin Magnesium Nanosheet for Improved Hydrogen Storage with Fishbone Shaped One-Dimensional Carbon Matrix29Influence of pore size and isosteric heat of adsorption of some metal–organic frameworks on the volumetric and gravimetric adsorption capacities of hydrogen at room temperature18Screening Study of the Effects of Impurity Gases on Hydrogen Storage in Metal-Organic Frameworks6Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution133High-Throughput Screening of Metal–Organic Frameworks for Ethane–Ethylene Separation Using the Machine Learning Technique52Spin-Crossover Effects in Reversible O2 Binding on a Dinuclear Cobalt(II) Complex7Machine Learning-Guided Equations for Super-Fast Prediction of Methane Storage Capacities of COFs4Machine Learning-Guided Equations for Super-Fast Prediction of Methane Storage Capacities of COFs1Concluding remarks: Cooperative phenomena in framework materials3High-Throughput Screening of Metal-Organic Frameworks for the Impure Hydrogen Storage Supplying to a Fuel Cell Vehicle11A review on current trends in potential use of metal-organic framework for hydrogen storage439In Silico Screening of Zeolites for High-Pressure Hydrogen Drying18Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties49The “Chemistree” of Porous Coordination Networks: Taxonomic Classification of Porous Solids to Guide Crystal Engineering Studies52Exploring Guest-Dependent Photoconductivity in a Donor-Containing Metal–Organic Framework8Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538New insights into hydrogen uptake on porous carbon materials via explainable machine learning156A Ranked-Orbital Approach to Select Active Spaces for High-Throughput Multireference Computation54Adsorption space for microporous polymers with diverse adsorbate species27Machine learning and descriptor selection for the computational discovery of metal-organic frameworks61Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240Assessment of Performance of Density Functionals for Predicting Potential Energy Curves in Hydrogen Storage Applications9Volumetrics of Hydrogen Storage by Physical Adsorption75Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures38Predicting hydrogen storage in MOFs via machine learning181Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures32Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques61Diversifying databases of metal organic frameworks for high-throughput computational screening0Metal-Organic Frameworks Promoted Hydrogen Storage Properties of Magnesium Hydride for In-Situ Resource Utilization (ISRU) on Mars19Applying design principles to improve hydrogen storage capacity in nanoporous materials6Machine learning approaches to rediscovery and optimization of hydrogen storage on porous bio-derived carbon88Parametrization of Nonbonded Force Field Terms for Metal–Organic Frameworks Using Machine Learning Approach17KCC-1 Supported CuCo Bimetal Catalysts for Promoting Hydrogen Production from Ammonia Borane Hydrolysis15Sequential design of adsorption simulations in metal–organic frameworks17Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening141Thermal Management for Hydrogen Charging and Discharging in a Screened Metal–Organic Framework Particle Tank35A New Machine Learning Framework for Efficient MOF Discovery: Application to Hydrogen Storage3From computational high-throughput screenings to the lab: taking metal–organic frameworks out of the computer28High-throughput computational screening of nanoporous materials in targeted applications65Hydrogen Encapsulation and Storage as an Alternative Energy Source0Metal-Organic Frameworks for Hydrogen Storage1Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage19In silico design of microporous polymers for chemical separations and storage8Recent Progress Using Solid-State Materials for Hydrogen Storage: A Short Review226Active subsets as a tool for structural characterisation and selection of metal-organic frameworks1Lattice Modification and Morphological Control of Halide-Substituted yqt-Type Zeolitic Imidazolate Frameworks Zn3mim5X, with X = F, Br, Cl, or OH5Improved de/re-hydrogenation properties of MgH2 catalyzed by graphene templated Ti–Ni–Fe nanoparticles39Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks75Machine-learning-based prediction of hydrogen adsorption capacity at varied temperatures and pressures for MOFs adsorbents51Fundamentals of hydrogen storage in nanoporous materials85Materials genome engineering-based hydrogen storage materialsdatabase and its applications6Probing the properties of molecules and complex materials using machine learning5Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning26Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture46Challenges to developing materials for the transport and storage of hydrogen534Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers76Computational Design of Metal–Organic Frameworks with Unprecedented High Hydrogen Working Capacity and High Synthesizability42Development of Porous Crystalline Materials for Selective Binding of O2 from Air2Active learning for efficient navigation of multi-component gas adsorption landscapes in a MOF22Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework3MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials111Machine learning-based discovery of vibrationally stable materials13

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

251 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Discovery and Optimization of Materials Using Evolutionary Approaches

    Chemical Reviews · 2016-05-12

    Tu C. Le, David Alan Winkler

    Read original paper ↗ ·

  2. Computational Modeling and Simulation of CO2Capture by Aqueous Amines

    Chemical Reviews · 2017-05-18

    Xin Yang, Robert J. Rees, William Conway, Graeme Puxty, Qi Yang, David Alan Winkler

    Read original paper ↗ ·

  3. Biomimetic molecular design tools that learn, evolve, and adapt

    Beilstein Journal of Organic Chemistry · 2017-06-29

    David Alan Winkler

    Read original paper ↗ ·

  4. Computational development of the nanoporous materials genome

    Nature Reviews Materials · 2017-07-04

    Peter G. Boyd, Yongjin Lee, Berend Smit

    Read original paper ↗ ·

  5. Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning

    Chemistry of Materials · 2017-08-25

    Jack D. Evans, François‐Xavier Coudert

    Read original paper ↗ ·

  6. Topologically Guided, Automated Construction of Metal–Organic Frameworks and Their Evaluation for Energy-Related Applications

    Crystal Growth & Design · 2017-09-11

    Yamil J. Colón, Diego A. Gómez‐Gualdrón, Randall Q. Snurr

    Read original paper ↗ ·

  7. DFT simulation of hydrogen storage on manganese phosphorous trisulphide (MnPS3)

    International Journal of Hydrogen Energy · 2017-11-08

    I. Cabria, Amin A. El-Meligi

    Read original paper ↗ ·

  8. Materials genomics‐guided ab initio screening of MOFs with open copper sites for acetylene storage

    AIChE Journal · 2017-11-08

    Ce Zhang, Youshi Lan, Xiangyu Guo, Qingyuan Yang, Chongli Zhong

    Read original paper ↗ ·

  9. Materials genome: research progress, challenges and outlook

    Scientia Sinica Chimica · 2017-12-08

    Yunqi Li, Lunyang Liu, Wenduo Chen, Lijia An

    Read original paper ↗ ·

  10. Text Mining Metal–Organic Framework Papers

    Journal of Chemical Information and Modeling · 2017-12-11

    Sanghoon Park, Baekjun Kim, Sihoon Choi, Peter G. Boyd, Berend Smit, Jihan Kim

    Read original paper ↗ ·

  11. An assessment of strategies for the development of solid-state adsorbents for vehicular hydrogen storage

    Energy & Environmental Science · 2018-01-01

    Mark D. Allendorf, Z. Hulvey, Thomas Gennett, Alauddin Ahmed, Tom S. Autrey, Jeffrey S. Camp, Eun Seon Cho, Hiroyasu Furukawa, Maciej Harańczyk, Martin Head‐Gordon, Sohee Jeong, Abhijeet Karkamkar, Di‐Jia Liu, Jeffrey R. Long, Katie R. Meihaus, Iffat H. Nayyar, Roman Nazarov, Donald J. Siegel, Vitalie Stavila, Jeffrey J. Urban, Srimukh Prasad Veccham, Brandon C. Wood

    Read original paper ↗ ·

  12. Hydrogen absorption in carbon allotropes and talc

    Kovove Materialy-Metallic Materials · 2018-01-01

    Jan Čermák, Lubomír Král

    Read original paper ↗ ·

  13. High-Throughput Computational Screening of the Metal Organic Framework Database for CH4/H2 Separations

    ACS Applied Materials & Interfaces · 2018-01-09

    Çiğdem Altıntaş, İlknur Eruçar, Seda Keskın

    Read original paper ↗ ·

  14. Effects of Force Field Selection on the Computational Ranking of MOFs for CO2 Separations

    Industrial & Engineering Chemistry Research · 2018-01-18

    Derya Dokur, Seda Keskın

    Read original paper ↗ ·

  15. Sparse QSAR modelling methods for therapeutic and regenerative medicine

    Journal of Computer-Aided Molecular Design · 2018-02-14

    David Alan Winkler

    Read original paper ↗ ·

  16. Enhanced $$\text {CO}_2$$ CO 2 selectivity within the cavity of gmelinite frameworks

    Adsorption · 2018-03-15

    Anastasios D. Gotzias, M.E. Kainourgiakis, Athanasios K. Stubos

    Read original paper ↗ ·

  17. Toward Effective Utilization of Methane: Machine Learning Prediction of Adsorption Energies on Metal Alloys

    The Journal of Physical Chemistry C · 2018-03-26

    Takashi Toyao, Keisuke Suzuki, Shoma Kikuchi, Satoru Takakusagi, Ken‐ichi Shimizu, Ichigaku Takigawa

    Read original paper ↗ ·

  18. Computer-aided discovery of a metal–organic framework with superior oxygen uptake

    Nature Communications · 2018-04-11

    Peyman Z. Moghadam, Timur İslamoğlu, Subhadip Goswami, Jason Exley, Marcus Fantham, Clemens F. Kaminski, Randall Q. Snurr, Omar K. Farha, David Fairen‐Jiménez

    Read original paper ↗ ·

  19. Present and future of MOF research in the field of adsorption and molecular separation

    Current Opinion in Chemical Engineering · 2018-05-17

    Camille Petit

    Read original paper ↗ ·

  20. Modeling Amorphous Microporous Polymers for CO2Capture and Separations

    Chemical Reviews · 2018-05-29

    Grit Kupgan, Lauren J. Abbott, Kyle E. Hart, Coray M. Colina

    Read original paper ↗ ·

  21. Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

    ChemRxiv · 2018-07-11

    Sherif Abdulkader Tawfik, Olexandr Isayev, Catherine Stampfl, Joseph George Shapter, David Alan Winkler, Michael J. Ford

    Read original paper ↗ ·

  22. Dynamic and Static Mechanical Properties of Crosslinked Polymer Matrices: Multiscale Simulations and Experiments

    Polymers · 2018-07-19

    Daria Victorovna Guseva, Vladimir Yu. Rudyak, Павел Вячеславович Комаров, Boris A. Bulgakov, Alexander V. Babkin, Alexander V. Chertovich

    Read original paper ↗ ·

  23. In Silico Screening of Metal–Organic Frameworks for Adsorption-Driven Heat Pumps and Chillers

    ACS Applied Materials & Interfaces · 2018-07-19

    Máté Erdős, Martijn F. de Lange, Freek Kapteijn, Othonas A. Moultos, Thijs J. H. Vlugt

    Read original paper ↗ ·

  24. Current status and development trend of wind power generation-based hydrogen production technology

    Energy Exploration & Exploitation · 2018-07-24

    Zheng Li, Peng Guo, Ruihua Han, Hexu Sun

    Read original paper ↗ ·

  25. Critical Factors in Computational Characterization of Hydrogen Storage in Metal–Organic Frameworks

    The Journal of Physical Chemistry C · 2018-07-26

    Jeffrey S. Camp, Vitalie Stavila, Mark D. Allendorf, David Prendergast, Maciej Harańczyk

    Read original paper ↗ ·

  26. A new approach for the prediction of partition functions using machine learning techniques

    The Journal of Chemical Physics · 2018-07-27

    Caroline Desgranges, Jérôme Delhommelle

    Read original paper ↗ ·

  27. Role of Pore Chemistry and Topology in the CO 2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning

    Chemistry of Materials · 2018-08-23

    Ryther Anderson, Jacob Rodgers, Edwin Argueta, Achay Biong, Diego A. Gómez‐Gualdrón

    Read original paper ↗ ·

  28. Role of simple descriptors and applicability domain in predicting change in protein thermostability

    PLoS ONE · 2018-09-07

    Kenneth N. McGuinness, Weilan Pan, Robert P. Sheridan, Grant S. Murphy, Alejandro Crespo

    Read original paper ↗ ·

  29. High-Throughput Screening of Metal–Organic Frameworks for Macroscale Heteroepitaxial Alignment

    ACS Applied Materials & Interfaces · 2018-10-29

    Andrew Tarzia, Masahide Takahashi, Paolo Falcaro, Aaron W. Thornton, Christian J. Doonan, David Mark Huang

    Read original paper ↗ ·

  30. Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks

    Molecular Systems Design & Engineering · 2018-11-01

    Benjamin J. Bucior, N. Scott Bobbitt, Timur İslamoğlu, Subhadip Goswami, Arun Gopalan, Taner Yildirim, Omar K. Farha, Neda Bagheri, Randall Q. Snurr

    Read original paper ↗ ·

  31. Attainable Volumetric Targets for Adsorption-Based Hydrogen Storage in Porous Crystals: Molecular Simulation and Machine Learning

    The Journal of Physical Chemistry C · 2018-12-05

    Grace C. Anderson, Benjamin Schweitzer, Ryther Anderson, Diego A. Gómez‐Gualdrón

    Read original paper ↗ ·

  32. Toward Automated Tools for Characterization of Molecular Porosity

    Journal of Chemical Theory and Computation · 2018-12-06

    Ismael Gómez García, Marco Bernabei, Maciej Harańczyk

    Read original paper ↗ ·

  33. Computational Tuning of the Paddlewheel tcb-MOF Family for Advanced Methane Sorption

    ACS Applied Energy Materials · 2018-12-20

    Mikhail V. Suyetin, Maxim V. Peskov, Udo Schwingenschlögl

    Read original paper ↗ ·

  34. Increasing topological diversity during computational “synthesis” of porous crystals: how and why

    CrystEngComm · 2019-01-01

    Ryther Anderson, Diego A. Gómez‐Gualdrón

    Read original paper ↗ ·

  35. Educational Modules on the Power-to-Gas Concept Demonstrate a Path to Renewable Energy Futures

    Journal of Chemical Education · 2019-01-10

    Isabel Rubner, Ashton J. Berry, Theodor Grofe, Marco Oetken

    Read original paper ↗ ·

  36. Metal‐organic Framework Sponges

    Unknown journal · 2019-02-13

    Sigurd Øien‐Ødegaard, Greig C. Shearer, Karl Petter Lillerud, Silvia Bordiga

    Read original paper ↗ ·

  37. Screening Diffusion of Small Molecules in Flexible Zeolitic Imidazolate Frameworks Using a DFT-Parameterized Force Field

    The Journal of Physical Chemistry C · 2019-03-06

    Ross J. Verploegh, Ambarish R. Kulkarni, Salah Eddine Boulfelfel, Jonathan C. Haydak, Dai Tang, David S. Sholl

    Read original paper ↗ ·

  38. Understanding Quantitative Relationship between Methane Storage Capacities and Characteristic Properties of Metal–Organic Frameworks Based on Machine Learning

    The Journal of Physical Chemistry C · 2019-03-22

    Xuanjun Wu, Sichen Xiang, Jiaqi Su, Weiquan Cai

    Read original paper ↗ ·

  39. Data mining new energy materials from structure databases

    Renewable and Sustainable Energy Reviews · 2019-03-25

    Lei Zhang, Zhiqiao Chen, Jing Su, Jingfa Li

    Read original paper ↗ ·

  40. The Role of Molecular Modeling & Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation

    ChemRxiv · 2019-03-27

    Árni Sturluson, Melanie T. Huynh, Alec R. Kaija, Caleb Laird, Sunghyun Yoon, Feier Hou, Zhenxing Feng, Christopher E. Wilmer, Yamil J. Colón, Yongchul G. Chung, Daniel W. Siderius, Cory M. Simon

    Read original paper ↗ ·

  41. Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage

    Molecular Simulation · 2019-04-04

    N. Scott Bobbitt, Randall Q. Snurr

    Read original paper ↗ ·

  42. Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

    Nature Communications · 2019-04-05

    Alauddin Ahmed, Saona Seth, Justin J. Purewal, Antek G. Wong‐Foy, Mike Veenstra, Adam J. Matzger, Donald J. Siegel

    Read original paper ↗ ·

  43. Structure-Mechanical Stability Relations of Metal-Organic Frameworks via Machine Learning

    Matter · 2019-05-15

    Peyman Z. Moghadam, Sven M. J. Rogge, Aurelia Li, Chun-Man Chow, Jelle Wieme, Noushin Moharrami, Marta Aragones‐Anglada, G. J. Conduit, Diego A. Gómez‐Gualdrón, Véronique Van Speybroeck, David Fairen‐Jiménez

    Read original paper ↗ ·

  44. Application of data science tools to determine feature correlation and cluster metal hydrides for hydrogen storage

    Materialia · 2019-06-06

    Alireza Rahnama, S. Sridhar

    Read original paper ↗ ·

  45. How to Design Hydrogen Storage Materials? Fundamentals, Synthesis, and Storage Tanks

    Advanced Sustainable Systems · 2019-06-26

    Qiwen Lai, Yahui Sun, Ting Wang, Poojan Modi, Claudio Cazorla, Umit B. Demirci, J.R. Ares, Fabrice Leardini, Kondo‐François Aguey‐Zinsou

    Read original paper ↗ ·

  46. Exploiting hydrophobicity and hydrophilicity in nanopores as a design principle for “smart” MOF microtanks for methane storage

    Molecular Systems Design & Engineering · 2019-07-22

    Ryther Anderson, Bomsaerah Seong, Zöe Peterson, Molly Stevanak, Moisés A. Carreón, Diego A. Gómez‐Gualdrón

    Read original paper ↗ ·

  47. The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation

    Molecular Simulation · 2019-08-08

    Árni Sturluson, Melanie T. Huynh, Alec R. Kaija, Caleb Laird, Sunghyun Yoon, Feier Hou, Zhenxing Feng, Christopher E. Wilmer, Yamil J. Colón, Yongchul G. Chung, Daniel W. Siderius, Cory M. Simon

    Read original paper ↗ ·

  48. Can the NIST Adsorption Database Be Used to Highlight Potential Materials for Gas Separation?

    ChemRxiv · 2019-08-14

    Paul Iacomi, Philip L. Llewellyn

    Read original paper ↗ ·

  49. Can the NIST Adsorption Database Be Used to Highlight Potential Materials for Gas Separation?

    ChemRxiv · 2019-08-14

    Paul Iacomi, Philip L. Llewellyn

    Read original paper ↗ ·

  50. Prediction of hydrogen adsorption in nanoporous materials from the energy distribution of adsorption sites

    Molecular Physics · 2019-08-28

    Arun Gopalan, Benjamin J. Bucior, N. Scott Bobbitt, Randall Q. Snurr

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.