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RESEARCH PAPER · Molecular Systems Design & Engineering · 2020

Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks

Zenan Shi, Wenyuan Yang, Xiaomei Deng, Chengzhi Cai, Yaling Yan, Hong Liang, Zili Liu, Zhiwei Qiao

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Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworksOriginalStructure–Property Correlation Study for Organic Photovoltaic Polymer Materials Using Data Science Approach33Machine learning and high-throughput computational screening of hydrophobic metal–organic frameworks for capture of formaldehyde from air83Machine-Learning-Based Prediction of Methane Adsorption Isotherms at Varied Temperatures for Experimental Adsorbents61High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials127Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks22Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks1Bayesian optimization of nanoporous materials112Concluding remarks: current and next generation MOFs23Current Research Trends and Perspectives on Solid-State Nanomaterials in Hydrogen Storage135Large-Scale Computational Screening of Metal-Organic Frameworks for D 2/H 2 Adsorption and Separation0Metal–organic frameworks for chemical sensing devices327Techno-economic analysis of metal–organic frameworks for adsorption heat pumps/chillers: from directional computational screening, machine learning to experiment48Inverse design of nanoporous crystalline reticular materials with deep generative models400Machine learning for advanced energy materials208Machine learning aided design of perovskite oxide materials for photocatalytic water splitting156A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Adsorption behavior of metal-organic frameworks: From single simulation, high-throughput computational screening to machine learning41Separation and Purification of Hydrocarbons with Porous Materials10Separation and Purification of Hydrocarbons with Porous Materials284Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design65Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Machine learning and descriptor selection for the computational discovery of metal-organic frameworks61Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240Hydroxylapatite and Related Minerals in Bone and Dental Tissues: Structural, Spectroscopic and Mechanical Properties from a Computational Perspective48Molecular fingerprint and machine learning to accelerate design of high‐performance homochiral metal–organic frameworks30Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures32Predicting Micropollutant Removal by Reverse Osmosis and Nanofiltration Membranes: Is Machine Learning Viable?154Machine learning and in-silico screening of metal–organic frameworks for O2/N2 dynamic adsorption and separation81Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials25Building a Library for Catalysts Research Using High‐Throughput Approaches45Machine learning for the design and discovery of zeolites and porous crystalline materials41Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Sequential design of adsorption simulations in metal–organic frameworks17Molecular-fingerprint machine-learning-assisted design and prediction for high-performance MOFs for capture of NMHCs from air51Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption50Data mining for predicting gas diffusivity in zeolitic-imidazolate frameworks (ZIFs)26Large-scale cascade cooling performance evaluation of adsorbent/water working pairs by integrated mathematical modelling and machine learning13Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting28Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage19RETRACTED: Robotics for control strategy optimization and simulation in automatic cigarette sorter1Insights into desiccant-based internally-cooled dehumidification using porous sorbents: From a modeling viewpoint35Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm707Large-scale computational screening of metal–organic frameworks for D2/H2 separation9Machine-Learning-Assisted High-Throughput computational screening of Metal–Organic framework membranes for hydrogen separation89High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration190Reverse-selective metal–organic framework materials for the efficient separation and purification of light hydrocarbons140Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-6656Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO2 from Flue Gas31MOF‐Based Chemiresistive Gas Sensors: Toward New Functionalities410Predicting hydrogen storage capacity of V–Ti–Cr–Fe alloy via ensemble machine learning67Finding the optimal CO2 adsorption material: Prediction of multi-properties of metal-organic frameworks (MOFs) based on DeepFM27Understanding Structure–Property Relationships of MOFs for Gas Sensing through Henry’s Constants14Connecting the dots for fundamental understanding of structure–photophysics–property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism9Active learning for efficient navigation of multi-component gas adsorption landscapes in a MOF22Discovery of Novel Photocatalysts Using Machine Learning Approach4Machine learning toward improving the performance of membrane-based wastewater treatment: A review53Machine learning-assisted prediction of water adsorption isotherms and cooling performance18The future of metal–organic frameworks and covalent organic frameworks: rational synthesis and customized applications35A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture35Accelerating Analysis for Structure Design via Deep Learning Surrogate Models14Machine Learning‐Aided Discovery of Nanoporous Materials for Energy‐ and Environmental‐Related Applications4Porous Molecular Materials1Prediction of methane adsorption isotherms in metal–organic frameworks by neural network synergistic with classical density functional theory27Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Interface chemistry of conductive crystalline porous thin films16Interpretable Machine‐Learning and Big Data Mining to Predict Gas Diffusivity in Metal‐Organic Frameworks68Automatic Machine Learning Combined with High-Throughput Computational Screening of Hydrophobic Metal–Organic Frameworks for Capture of Methanol and Ethanol from the Air12Unified roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and their Impact on Particle Size and Morphology2Active Learning for Adsorption Simulations: Evaluation, Criteria Analysis, and Recommendations for Metal–Organic Frameworks20Advances in data‐assisted high‐throughput computations for material design112Machine learning aided evaluation and design based on polymer membrane materials3Host–guest interactions in framework materials: Insight from modeling28Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective43Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers3Evaluating CH 4 /N 2 Separation Performances of Hundreds of Thousands of Real and Hypothetical MOFs by Harnessing Molecular Modeling and Machine Learning16High-throughput computational screening and molecular fingerprint design of metal-organic framework adsorbents for separation of C3 components7Drug delivery system tailoring via metal-organic framework property prediction using machine learning: A disregarded approach20Computational screening of metal–organic frameworks for environmental protection0Machine learning approaches for screening of materials in flexible electronic devices0Self-Consistency Is Not Enough: A Framework for Error-Correcting ML Potentials with On-the-Fly Residual Learning0Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and Mofs Using Machine Learning1Machine-learning-driven discovery of metal–organic framework adsorbents for hexavalent chromium removal from aqueous environments26Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials24Improving performance prediction of metal-organic framework membranes for reverse osmosis via genetic algorithm optimized artificial neural networks8Recent advances in machine learning interatomic potentials for cross-scale computational simulation of materials18Advances in hydrogen storage materials: harnessing innovative technology, from machine learning to computational chemistry, for energy storage solutions270From Synthesis Conditions to UiO-66 Properties: Machine Learning Approach15Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks46Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery76Machine learning insights into predicting biogas separation in metal-organic frameworks15Combining computational screening and machine learning to explore MOFs and COFs for methane purification11Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology48Exploring factors affecting the dissociation energies of C–O and C–C bonds in lignin oligomers21Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery29Data-Driven and Machine Learning to Screen Metal–Organic Frameworks for the Efficient Separation of Methane11Unifying mixed gas adsorption in molecular sieve membranes and MOFs using machine learning15Machine Learning Assisted Discovery of Efficient MOFs for One-Step C2H4 Purification from Ternary C2H2/C2H4/C2H6 Mixtures3Current update and prospects in the development of conductive metal-organic framework electrodes for lithium-based batteries13Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal–Organic Frameworks for Hydrogen Storage26Porous materials0Combining machine learning and molecular simulation to explore MOF materials that contribute to CF4/N2 separation9Self-Driving Laboratories for Chemistry and Materials Science674Accelerated Discovery of CO 2 Solid Sorbents Using Active Machine Learning: Review and Perspectives18Targeted metal–organic framework discovery goes digital: machine learning’s quest from algorithms to atom arrangements27Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions272Many-body expansion based machine learning models for octahedral transition metal complexes2Covalent integration of polymers and porous organic frameworks20A SYSTEMATIC LITERATURE REVIEW ON ENERGY-EFFICIENT TRANSFORMER DESIGN FOR SMART GRIDS0AI-driven advances in metal–organic frameworks: from data to design and applications47Generative AI for design of nanoporous materials: review and future prospects41Negative Knowledge Suppression in Autonomous Materials Engineering: Archival Governance Failures0Sustainable carbon materials in environmental and energy applications47Artificial Intelligence in Metal–Organic Frameworks from 2013 to 2024: A Bibliometric Analysis10Hydrogen storage via adsorption: A review of recent advances and challenges62Application of machine learning in adsorption energy storage using metal organic frameworks: A review17Machine Learning for Gas Adsorption in Metal–Organic Frameworks: A Review on Predictive Descriptors69A Multi-Method Approach to Analyzing MOFs for Chemical Warfare Simulant Capture: Molecular Simulation, Machine Learning, and Molecular Fingerprints9Machine learning-assisted development of gas separation membranes: A review13Metal-organic frameworks for low-concentration gases adsorption under ambient conditions: Characterization, modification, processing, shaping and applications35Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review41Pressure swing adsorption for oxygen production: Adsorbents, reactors, processes and perspective30High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys8Assessing UFF and DFT-Tuned Force Fields for Predicting Experimental Isotherms of MOFs14Impact of carbon dioxide loading on the thermal conductivity of metal organic frameworks5Machine learning-guided design of metal-organic frameworks for efficient H2S/CH4 separation4Generating New Coordination Compounds via Multireference Simulations, Genetic Algorithms, and Machine Learning: The Case of Co(II) and Dy(III) Molecular Magnets7A Machine Learning-Boosted High-Throughput Screening of Metal–Organic Frameworks for Ethane/Ethylene Separation: From Molecular Simulation to Process Modeling4A review of molecular dynamic simulation on polymer electrolyte membrane fuel cell20Leveraging advanced ensemble learning techniques for methane uptake prediction in metal organic frameworks2Computational screening of covalent organic frameworks for He purification with adsorption or membrane separation16Bridging classical and intelligent models: machine learning-guided adsorption isotherms for tailored acetone capture on porous carbon3Synthesis and characterization of novel bio-energetic polybenzoxazines derived from eugenol0Data-driven strategy for size-controllable growth of tin(II) sulfide nanoplates via machine learning0Advancing metal organic framework and covalent organic framework design via the digital-intelligent paradigm18De novo multi-objective generation framework for energetic materials with trading off energy and stability13Next generation hydrogen from sunlight: ZIF-8 nanohybrids and the rise of sustainable catalysis6Molecular Simulation of Hydrogen Systems: From Properties and Methods to Applications and Future Directions10Beyond batteries: Architecting materials through surface chemistry and engineering for revolutionizing hydrogen storage3Machine-learning-guided design of MOF-based electrocatalysts for sustainable ammonia production19The future of MOFs0A review on metal–organic framework-based polymer solid-state electrolytes for energy storage2Recent advances in coordination chemistry-driven metal-organic frameworks for cancer drug delivery and therapeutics16Research Progress in Machine Learning Techniques for Metal–Organic Framework Screening4Machine learning-driven design of CO2 capture materials: A focus on progress and challenges for MOFs and ILs2MOFMeld: a structure–language fusion framework for MOF property prediction in carbon capture1Process-informed optimal design principles for cooperative step-isotherm adsorbents in direct air capture using ANN-accelerated TVSA modelling0Synergistic machine learning and molecular simulation for xenon adsorption and separation: Mechanisms, screening, and performance validation0Deciphering the multivariable interactions of MOFs in heavy metal adsorption via explainable machine learning for sustainable and resource-efficient adsorbent development2Innovative linker design and synthetic approaches in MOFs and COFs for next-generation functional materials1Machine Learning for Gas Capture in Ionic Liquids: Current Status and Future Trends0

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