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RESEARCH PAPER · Molecular Simulation · 2019

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

N. Scott Bobbitt, Randall Q. Snurr

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Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storageOriginalUnderstanding the role of linker flexibility in soft porous coordination polymers15An easy way to identify high performing covalent organic frameworks for hydrogen storage23Selecting Adsorbents to Separate Diverse Near-Azeotropic Chemicals33The Chemistry of Reticular Framework Nanoparticles: MOF, ZIF, and COF Materials350Porous Material Screening and Evaluation for Deep Desulfurization of Dry Air13Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Four-dimensional metal-organic frameworks153Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method66Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Renewable hydrogen for the chemical industry161Transfer Learning Study of Gas Adsorption in Metal–Organic Frameworks108Recent advances, opportunities, and challenges in high-throughput computational screening of MOFs for gas separations255Screening Study of the Effects of Impurity Gases on Hydrogen Storage in Metal-Organic Frameworks6A fast procedure for the estimation of the hydrogen storage capacity by cryoadsorption of metal-organic framework materials from their available porous properties5Accelerating Discovery of Metal–Organic Frameworks for Methane Adsorption with Hierarchical Screening and Deep Learning74Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks22Unsupervised machine learning methods for polymer nanocomposites data via molecular dynamics simulation13Solid-state hydrogen storage as a future renewable energy technology6Adsorption-Based Separation of Near-Azeotropic Mixtures—A Challenging Example for High-Throughput Development of Adsorbents12Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240Chembot: A Machine Learning Approach to Selective Configuration Interaction31Database Creation, Visualization, and Statistical Learning for Polymer Li+-Electrolyte Design32Predicting 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 mixtures32Machine learning-based discovery of molecules, crystals, and composites: A perspective review16Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques61Computational Screening of Metal-Catecholate-Functionalized Metal–Organic Frameworks for Room-Temperature Hydrogen Storage27Entropy-based active learning of graph neural network surrogate models for materials properties34Computational Screening of MOFs and Zeolites for Direct Air Capture of Carbon Dioxide under Humid Conditions71Sequential design of adsorption simulations in metal–organic frameworks17Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal–Organic Frameworks5Comprehensive Assessment of the Accuracy of the Ideal Adsorbed Solution Theory for Predicting Binary Adsorption of Gas Mixtures in Porous Materials44Incorporating Flexibility Effects into Metal–Organic Framework Adsorption Simulations Using Different Models59Prediction of O 2 /N 2 Selectivity in Metal–Organic Frameworks via High-Throughput Computational Screening and Machine Learning72Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials23High-Throughput Computational Screening of Hypothetical Metal-Organic Frameworks with Open Copper Sites for Co2/H2 Separation1High-throughput computational screening of hypothetical metal–organic frameworks with open copper sites for CO 2 /H 2 separation18High-throughput computational screening of nanoporous materials in targeted applications65Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores3Design of portable hydrogen tank using adsorption material as storage media: An alternative to Type IV compressed tank54Interfacial thermal resistance in polymer composites: a molecular dynamic perspective19Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization35Recent Development in Nanoconfined Hydrides for Energy Storage25Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO 2 Capture: Machine Learning and DFT Calculation Approaches46MOF‐Based Chemiresistive Gas Sensors: Toward New Functionalities410Prediction of peptide-induced silica formation under a wide pH range by molecular descriptors0Room-temperature hydrogen storage performance of metal-organic framework/graphene oxide composites by molecular simulations40In Silico Screening of Metal-Organic Frameworks for Formaldehyde Capture with and without Humidity by Molecular Simulation7A process-level perspective of the impact of molecular force fields on the computational screening of MOFs for carbon capture32Active learning for efficient navigation of multi-component gas adsorption landscapes in a MOF22Furtherance of the material-based hydrogen storage based on theory and experiments85MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials111Atomistic simulation of soft porous coordination polymers5Recent advancements in hydrogen storage - Comparative review on methods, operating conditions and challenges262Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic20Machine learning assisted predictions for hydrogen storage in metal-organic frameworks92High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption26Efficient Exploration of Adsorption Space for Separations in Metal–Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory14On the shoulders of high-throughput computational screening and machine learning: Design and discovery of MOFs for H2 storage and purification46Host–guest interactions in framework materials: Insight from modeling28Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage7Drug delivery system tailoring via metal-organic framework property prediction using machine learning: A disregarded approach20Deep learning-based recommendation system for metal–organic frameworks (MOFs)15Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and Mofs Using Machine Learning1Role of metal-organic framework in hydrogen gas storage: A critical review124High-Throughput Screening of Covalent Organic Frameworks for Carbon Capture Using Machine Learning78Advancing hydrogen storage predictions in metal-organic frameworks: A comparative study of LightGBM and random forest models with data enhancement57Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery76Long Duration Energy Storage Using Hydrogen in Metal–Organic Frameworks: Opportunities and Challenges61Machine learning based screening of organic frameworks for separation of CF4/N2, C2F6/N2, and SF6/N219Engineering Machine Learning Features to Predict Adsorption of Carbon Dioxide and Nitrogen in Metal–Organic Frameworks33Unifying mixed gas adsorption in molecular sieve membranes and MOFs using machine learning15Unraveling the Potential of Solid-State Hydrogen Storage Materials: Insights from First Principle Calculations57Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal–Organic Frameworks for Hydrogen Storage26Prediction of hydrogen storage in metal-organic frameworks using CatBoost-based approach26Interpretable machine learning for materials discovery: Predicting CO2 adsorption properties of metal–organic frameworks28AI-driven development of high-performance solid-state hydrogen storage42Accelerated convergence via adiabatic sampling for adsorption and desorption processes2Multi-Scale Computational Design of Metal–Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization29Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs8Understanding stable adsorption states in flexible soft porous coordination polymers through free energy profiles1Broad range material-to-system screening of metal–organic frameworks for hydrogen storage using machine learning16A comprehensive review of reduction technologies for tetrafluoromethane (CF4): Progress, challenges and future directions11Exploring advanced artificial intelligence techniques for efficient hydrogen storage in metal organic frameworks12Energy advancements and integration strategies in hydrogen and battery storage for renewable energy systems66A review on machine learning applications in hydrogen energy systems49Implementation of Genetic Algorithms to Optimize Metal–Organic Frameworks for CO 2 Capture10Review of hydrogen storage sustainability using bibliometric and mathematical modeling analysis5Facilitating Screening of MOFs for Mixed Matrix Membranes Using Machine Learning and the Maxwell Model9Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity13Artificial intelligence-based prediction of hydrogen uptake of metal organic frameworks with high volumetric storage density for fuel cell systems3FusionStackBoost: A machine learning approach for accurate prediction of hydrogen storage performance in metal organic frameworks7Machine learning potentials for hydrogen absorption in TiCr2 Laves phases9Multitask transfer learning framework for predicting hydrogen storage performance of metal/covalent-organic frameworks across diverse operating conditions8AI-driven advances in composite materials for hydrogen storage vessels: A review19MIL-101 (Fe) and its nanocomposites with MWCNT and GO, for electrochemical hydrogen storage3Advancing metal organic framework and covalent organic framework design via the digital-intelligent paradigm18Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials3Effects of Temperature and Methane Adsorption on the Free Energy Profiles of Soft Porous Coordination Polymers0Machine learning accelerates the development of metal–organic frameworks0Molecular Simulation of Hydrogen Systems: From Properties and Methods to Applications and Future Directions10Combining chemical, geometric, and novel topological features to develop generalizable machine learning models for predicting mechanically stable MOFs2Packing efficiency governs CO 2 /H 2 selectivity in machine-learning-screened metal–organic frameworks0XPEAK: an XRD-driven machine learning platform for predicting the catalyst-enhanced dehydrogenation peak temperature of MgH 28Frameworks for the Future: Metal Organic Frameworks and their Composites based solutions for Hydrogen Storage3Computational intelligence approaches for hydrogen storage material design3Reassessed Ability of Carbon-Based Physisorbing Materials to Keep Pace with Evolving Practical Targets for Hydrogen Storage0Artificial intelligence shaping the future of hydrogen technologies6Artificial intelligence-facilitated multiscale theoretical modelling to advance battery science2Phase equilibrium modeling advances subsurface energy transition for a carbon-resilient future1Deep learning-based masked autoencoder predicts CO2 working capacity of metal–organic frameworks0Rapid Exploration of Mixture Adsorption via Adiabatic Sampling0Low Confinement and Strong Pore Hydrophilicity Drive Intrinsic Water Permeability Inside Cage-Like Metal–Organic Frameworks0Toward viable H2 storage in Ca decorated low-dimensional materials with insights from reference quantum Monte Carlo0Machine learning-guided design of engineered metal-organic frameworks for sustainable hydrogen storage and production0

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