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RESEARCH PAPER · The Journal of Physical Chemistry C · 2019

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

Xuanjun Wu, Sichen Xiang, Jiaqi Su, Weiquan Cai

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Understanding Quantitative Relationship between Methane Storage Capacities and Characteristic Properties of Metal–Organic Frameworks Based on Machine LearningOriginalThe Role of Molecular Modeling & Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation0The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation123Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials76Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks158Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air78Selecting Adsorbents to Separate Diverse Near-Azeotropic Chemicals33Using Machine Learning to Predict the Dissociation Energy of Organic Carbonyls30Revealing enhancement mechanism of volumetric hydrogen storage capacity of nano-porous frameworks by molecular simulation17Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Computational screening of metal-organic frameworks with open copper sites for hydrogen purification20Applications of machine learning in metal-organic frameworks225Machine Learning Prediction of Electronic Coupling between the Guanine Bases of DNA28Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks22Hydrogen bonded frameworks: smart materials used smartly43Adsorption-Based Separation of Near-Azeotropic Mixtures—A Challenging Example for High-Throughput Development of Adsorbents12Adsorption behavior of metal-organic frameworks: From single simulation, high-throughput computational screening to machine learning41Boosting algorithms in energy research: a systematic review88Machine learning and descriptor selection for the computational discovery of metal-organic frameworks61Interpretable Machine Learning-Based Predictions of Methane Uptake Isotherms in Metal–Organic Frameworks62High‐throughput computational screening of porous polymer networks for natural gas sweetening based on a neural network22Predicting crop root concentration factors of organic contaminants with machine learning models57Prediction of O 2 /N 2 Selectivity in Metal–Organic Frameworks via High-Throughput Computational Screening and Machine Learning72Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption50Metal–Organic Frameworks: Promising Materials for Methane Storage0Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting28Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores3Chemistry-Encoded Convolutional Neural Networks for Predicting Gaseous Adsorption in Porous Materials52Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling43Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation74Predicting pesticide dissipation half-life intervals in plants with machine learning models44Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization35Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks75X-ray Diffraction and Molecular Simulations in the Study of Metal–Organic Frameworks for Membrane Gas Separation13Combining Computational Screening and Machine Learning to Predict Metal–Organic Framework Adsorbents and Membranes for Removing CH4 or H2 from Air14Machine learning prediction of open metal sites in metal-organic framework catalysts1Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture46A Self-Evolutionary Methodology for Reverse Design of Novel MOFs9Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework3Machine Learning‐Aided Discovery of Nanoporous Materials for Energy‐ and Environmental‐Related Applications4Metal–Organic Frameworks for Water Harvesting and Concurrent Carbon Capture: A Review for Hygroscopic Materials175Prediction of methane adsorption isotherms in metal–organic frameworks by neural network synergistic with classical density functional theory27Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials55Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Analyzing of metal organic frameworks performance in CH4 adsorption using machine learning techniques: A GBRT model based on small training dataset33Active Learning for Adsorption Simulations: Evaluation, Criteria Analysis, and Recommendations for Metal–Organic Frameworks20Implementing and understanding the unsupervised transfer learning in metal organic framework toward methane adsorption from hypothetical to experimental data13Host–guest interactions in framework materials: Insight from modeling28Direct prediction of gas adsorption via spatial atom interaction learning56Application of machine learning in MOFs for gas adsorption and separation31Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage7Deep learning-based recommendation system for metal–organic frameworks (MOFs)15Prediction of Hydrogen Adsorption and Moduli of Metal–Organic Frameworks (MOFs) Using Machine Learning Strategies26From Synthesis Conditions to UiO-66 Properties: Machine Learning Approach15New Benchmark Metal-Organic Framework for Ambient Hydrogen Storage: MOF-20871Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks46Machine learning insights into predicting biogas separation in metal-organic frameworks15Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space32Porous materials0Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications79From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks91Targeted metal–organic framework discovery goes digital: machine learning’s quest from algorithms to atom arrangements27Machine learning for gas–solid interaction materials and devices10Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions272Machine learning approaches for the prediction of hydrogen uptake in metal-organic-frameworks: A comprehensive review18Emerging MOF-based antibiotic detection methods in water environments: Recent advances, challenges, and prospects17Unveiling cutting-edge progress in coordination chemistry of the metal-organic frameworks (MOFs) and their composites: Fundamentals, synthesis strategies, electrochemical and environmental applications39Kernel regression methods for prediction of materials properties: Recent developments22Integrating Molecular Simulations with Machine Learning to Discover Selective MOFs for CH4/H2 Separation3The promising path of Oral MOFs: Redefining strategies for gastrointestinal detoxification5Toward accurate prediction of N2 uptake capacity in metal-organic frameworks2Interpretable machine learning assisted screening of metal-organic frameworks for methane adsorption mechanism exploration under low and high pressures2Application of multi-task learning in analysing the methane working capacity of metal–organic frameworks1Artificial intelligence and IR 4.0 applications in carbon capture and storage0The Application of Metal–Organic Frameworks as Drug Delivery Systems: From the Perspective of Molecular Dynamics Simulations2Metal-organic frameworks based electrocatalysts for oxygen evolution: electrochemical fundamentals, structure-activity relations, and AI-guided design5

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

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  3. Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

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  5. Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air

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  12. Machine Learning Prediction of Electronic Coupling between the Guanine Bases of DNA

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  14. Hydrogen bonded frameworks: smart materials used smartly

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  17. Boosting algorithms in energy research: a systematic review

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  18. Machine learning and descriptor selection for the computational discovery of metal-organic frameworks

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  19. Interpretable Machine Learning-Based Predictions of Methane Uptake Isotherms in Metal–Organic Frameworks

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