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RESEARCH PAPER · Nature Communications · 2020

Understanding the diversity of the metal-organic framework ecosystem

Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, Daniele Ongari, Jon Paul Janet, Peter G. Boyd, Yongjin Lee, Berend Smit, Heather J. Kulik

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Understanding the diversity of the metal-organic framework ecosystemOriginalBig-Data Science in Porous Materials: Materials Genomics and Machine Learning599Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution133The Role of Machine Learning in the Understanding and Design of Materials433Materials Informatics with PoreBlazer v4.0 and the CSD MOF Database352A new strategy for constructing covalently connected MOF@COF core–shell heterostructures for enhanced photocatalytic hydrogen evolution146Bayesian optimization of nanoporous materials112Engineering metal–organic frameworks for adsorption-based gas separations: from process to atomic scale64Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles40Performance-Based Screening of Porous Materials for Carbon Capture277Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design55Toward smart carbon capture with machine learning121Metal–Organic Frameworks for Xylene Separation: From Computational Screening to Machine Learning66Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538A Review of Nanostructured Ion‐Exchange Membranes44Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks69What Lies beneath a Metal–Organic Framework Crystal Structure? New Design Principles from Unexpected Behaviors79Machine learning and descriptor selection for the computational discovery of metal-organic frameworks61Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches72C2s/C1 hydrocarbon separation: The major step towards natural gas purification by metal-organic frameworks (MOFs)145Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Machine learning and in-silico screening of metal–organic frameworks for O2/N2 dynamic adsorption and separation81Ab Initio Machine Learning in Chemical Compound Space195Recent Improvement Strategies on Metal-Organic Frameworks as Adsorbent, Catalyst, and Membrane for Wastewater Treatment62Elaboration of Porous Salts53Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks198Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning103Realizing the data-driven, computational discovery of metal-organic framework catalysts61Machine‐Learning Prediction of Metal–Organic Framework Guest Accessibility from Linker and Metal Chemistry74Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening141A critical review on the development of metal-organic frameworks for boosting photocatalysis in the fields of energy and environment102Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery98A review on preparation methods and applications of metal–organic framework-based solid-phase microextraction coatings64Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption50Designing optimal core–shell MOFs for direct air capture42High-throughput computational screening of nanoporous materials in targeted applications65Machine learning reveals key ion selectivity mechanisms in polymeric membranes with subnanometer pores136Encapsulation, Release, and Cytotoxicity of Doxorubicin Loaded in Liposomes, Micelles, and Metal-Organic Frameworks: A Review142MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**237Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling43Graph entropies, enumeration of circuits, walks and topological properties of three classes of isoreticular metal organic frameworks41Metal‐Organic‐Framework as Novel Electrode Materials for Hybrid Battery‐Supercapacitor Applications39MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks135Machine Learning for the Discovery, Design, and Engineering of Materials66Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation74Trace removal of benzene vapour using double-walled metal–dipyrazolate frameworks286Recent advances in heterogeneous catalysts for the synthesis of alkyl levulinate biofuel additives from renewable levulinic acid: A comprehensive review60Cobalt-based MOF nanoribbons with abundant O/N species for cycloaddition of carbon dioxide to epoxides44Low-dimensional assemblies of metal-organic framework particles and mutually coordinated anisotropy84Metal–Organic Frameworks and Covalent Organic Frameworks: Emerging Advances and Applications83Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation45Approaches to Enhancing Electrical Conductivity of Pristine Metal–Organic Frameworks for Supercapacitor Applications114Machine learning enables interpretable discovery of innovative polymers for gas separation membranes237Engineering the ZIF-8 Pore for Electrochemical Sensor Applications─A Mini Review165Highly scalable and pH stable 2D Ni-MOF-based composites for high performance supercapacitor113Metal–Organic Frameworks for CO 2 Separation from Flue and Biogas Mixtures132Two-dimensional metal-organic frameworks: From synthesis to biomedical, environmental, and energy conversion applications65Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis140Ultrasonically synthesized MOFs for modification of polymeric membranes: A critical review65Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture46Solid-State Proton Conduction Driven by Coordinated Water Molecules in Metal–Organic Frameworks and Coordination Polymers121Metal–Organic Frameworks as Electrocatalysts243Metal–Organic Frameworks as Electrocatalysts41Metal‐Organic Frameworks for Greenhouse Gas Applications53Deep learning metal complex properties with natural quantum graphs59Low-concentration CO 2 capture using metal–organic frameworks – current status and future perspectives56Recent advances in the synthesis and catalytic applications of metal–organic framework/covalent organic framework composites50ReDD-COFFEE: a ready-to-use database of covalent organic framework structures and accurate force fields to enable high-throughput screenings54Understanding and controlling the nucleation and growth of metal–organic frameworks186Copper-Based Metal–Organic Frameworks (MOFs) as an Emerging Catalytic Framework for Click Chemistry75Metal-organic framework hybrid adsorbents for carbon capture – A review92ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning150Metal-organic framework-derived transition metal chalcogenides (S, Se, and Te): Challenges, recent progress, and future directions in electrochemical energy storage and conversion systems201Metal‐Organic Frameworks for Photocatalytic Water Splitting and CO 2 Reduction69Metal‐Organic Frameworks for Photocatalytic Water Splitting and CO2 Reduction414MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction163Experimentally Validated Ab Initio Crystal Structure Prediction of Novel Metal–Organic Framework Materials44Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials55Exploiting a Multi-Responsive Oxadiazole Moiety in One Three-Dimensional Metal–Organic Framework for Remedies to Three Environmental Issues38Road Map for In Situ Grown Binder‐Free MOFs and Their Derivatives as Freestanding Electrodes for Supercapacitors58Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO 2 ’s Chemisorption and Diffusion in Mg-MOF-7488Recent advances in metal/covalent organic frameworks based materials: Their synthesis, structure design and potential applications for hydrogen production153An Ecosystem for Digital Reticular Chemistry52Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks208Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217Recent developments in 2D materials for energy harvesting applications43A database of ultrastable MOFs reassembled from stable fragments with machine learning models89CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks40Quantification of Linker Defects in UiO-Type Metal–Organic Frameworks94DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining86Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening39pH-stable MOFs: Design principles and applications196Environmentally benign bioderived, biocompatible, thermally stable MOFs suitable for food contact applications65Mitigating metal-organic framework (MOF) toxicity for biomedical applications305Metal-organic frameworks for wastewater treatment: Recent developments, challenges, and future prospects48The role of dynamic metal-ligand bonds in metal-organic framework chemistry117Green Synthesis of Reticular Materials56Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future47Integrating stability metrics with high-throughput computational screening of metal–organic frameworks for CO2 capture48Clinical translation of metal–organic frameworks81Application of interpretable machine learning models to improve the prediction performance of ionic liquids toxicity50Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective43Metal–Organic Frameworks for the Enhancement of Lithium‐Based Batteries: A Mini Review on Emerging Functional Designs45Large‐Scale Production of Metal–Organic Frameworks421Enhancing Structure–Property Relationships in Porous Materials through Transfer Learning and Cross-Material Few-Shot Learning44Biomedical Metal–Organic Framework Materials: Perspectives and Challenges188Accelerated chemical science with AI86Image and data mining in reticular chemistry powered by GPT-4V68Inverse design of metal–organic frameworks for direct air capture of CO 2 via deep reinforcement learning73MOFs materials as photocatalysts for CO2 reduction: Progress, challenges and perspectives69Recent Progress and Prospect of Metal–Organic Framework-Based Nanozymes in Biomedical Application71Trends in nanomaterial types, synthesis methods, properties and uses: Toxicity, environmental concerns and economic viability120Leveraging large language models for predictive chemistry367Band gap analysis in MOF materials: Distinguishing direct and indirect transitions using UV–vis spectroscopy191A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture104A roadmap of MOFs derived porous carbon, oxides, chalcogenides, and phosphides of metals: Synthesis, properties, parameter modulation and their utilization as an electrode for Li/Na/K-ion batteries54A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks122Adsorption of pharmaceuticals from water using metal-organic frameworks (MOFs), MOF-derived carbons, covalent-organic frameworks (COFs), COF-derived carbons: Comparison of the four adsorbents123Zeolitic imidazolate framework-8: a versatile nanoplatform for tissue regeneration54A cobalt-based layered MOF material for supercapacitor applications62High-Throughput Screening of Covalent Organic Frameworks for Carbon Capture Using Machine Learning78The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture99Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery76Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology48Synergistic sorption strategies: Ionic liquids-modified MOF matrices for adsorption processes42MOF-derived LDHs: Unveiling their potential in oxygen evolution reaction48ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models236Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set88Construction of Porous Aromatic Frameworks with Specifically Designed Motifs for Charge Storage and Transport50Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications79Nanoparticle-integrated metal–organic frameworks: a revolution in next-generation drug delivery systems44Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks91Metal-organic framework (MOF)-based materials for photocatalytic antibacterial applications115Recent Advancement in 2D Metal–Organic Framework for Environmental Remediation: A Review48Precision-Engineered Construction of Proton-Conducting Metal–Organic Frameworks99CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening106Hybrid Metal-Organic Frameworks (MOFs) for Various Catalysis Applications39Generative AI for design of nanoporous materials: review and future prospects41MOSAEC-DB: a comprehensive database of experimental metal–organic frameworks with verified chemical accuracy suitable for molecular simulations44Recent advances in Fe-based metal-organic frameworks: Structural features, synthetic strategies and applications79Large language models for reticular chemistry133Conductive Metal–Organic Frameworks and Their Electrocatalysis Applications81Identifying MOFs for electrochemical energy storage via density functional theory and machine learning51Microfluidic synthesis of MOFs, COFs, and HOFs: Insights and advances41Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research54Connecting metal-organic framework synthesis to applications using multimodal machine learning40Recent developments in the field of MOF-based catalytic systems: A review41Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50

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