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RESEARCH PAPER · Journal of the American Chemical Society · 2021

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks

Aditya Nandy, Chenru Duan, Heather J. Kulik

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Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic FrameworksOriginalRealizing the data-driven, computational discovery of metal-organic framework catalysts61Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery98Application of transfer learning to predict diffusion properties in metal–organic frameworks18Catalytic properties of the ferryl ion in the solid state: a computational review2High-throughput computational screening of nanoporous materials in targeted applications65Data-Driven Matching of Experimental Crystal Structures and Gas Adsorption Isotherms of Metal–Organic Frameworks24MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis21MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks135Machine Learning for the Discovery, Design, and Engineering of Materials66Recent progress, mechanisms, and perspectives for crystal and interface chemistry applying to the Zn metal anodes in aqueous zinc‐ion batteries157Integrated metal‐organic framework ( MOF ) and pressure/vacuum swing adsorption process design: MOF matching24Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0Understanding the Structural Collapse during Activation of Metal–Organic Frameworks with Copper Paddlewheels23Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation45Prediction and design of cyclodextrin inclusion complexes formation via machine learning-based strategies26MOF‐Based Chemiresistive Gas Sensors: Toward New Functionalities410An ecosystem for digital reticular chemistry4Computation-informed optimization of Ni(PyC)2 functionalization for noble gas separations12A data-science approach to predict the heat capacity of nanoporous materials117MOFTransformer: A Multi-modal Pre-training Transformer for Universal Transfer Learning in Metal-Organic Frameworks0Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture46The Effects of Ligand Substitution on MOF-808 Thermal Cycling Stability and Negative Thermal Expansion15Connecting the dots for fundamental understanding of structure–photophysics–property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism9Airborne Toluene Detection Using Metal–Organic Frameworks28A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation24Covalent organic frameworks for CO 2 capture: from laboratory curiosity to industry implementation191Effects of MOF linker rotation and functionalization on methane uptake and diffusion26Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models4Low-cost machine learning prediction of excited state properties of iridium-centered phosphors40Molecular dynamics-machine learning approaches for the accurate prediction of electrochemical windows of ionic liquid electrolytes for dual-ion batteries34Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations41Practical Computational Chemistry Course for a Comprehensive Understanding of Organic, Inorganic, and Physical Chemistry: From Molecular Interactions to Chemical Reactions15Machine Learning‐Aided Discovery of Nanoporous Materials for Energy‐ and Environmental‐Related Applications4MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction163Bridging the complexity gap in computational heterogeneous catalysis with machine learning280An 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 frameworks208High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap35Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217A database of ultrastable MOFs reassembled from stable fragments with machine learning models89PMTransformer: Universal Transfer Learning and Cross-material Few-shot Learning in Porous Materials1Methods, progresses, and opportunities of materials informatics72Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit39Computational Discovery of Stable Metal–Organic Frameworks for Methane-to-Methanol Catalysis34Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes21Material Property Prediction by Integrating Literature-mining and Crossed Feature Selection0Modeling ionic conductivity and activation energy in garnet-structured solid electrolytes: The role of composition, grain boundaries and processing16ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis536Metal-solvent interaction contribution on voltage for metal ion battery: An interpretable machine learning approach10Database System Based on 3Dmax Big Data Mining Technology1A New Era of Modeling MOF‐Based Membranes: Cooperation of Theory and Data Science25On the shoulders of high-throughput computational screening and machine learning: Design and discovery of MOFs for H2 storage and purification46A GPT‐4 Reticular Chemist for Guiding MOF Discovery**162A GPT‐4 Reticular Chemist for Guiding MOF Discovery**13Comment on ‘Physics-based representations for machine learning properties of chemical reactions’10Organic pollutants removal from aqueous solutions using metal-organic frameworks (MOFs) as adsorbents: A review93Machine learning-based evaluation of functional characteristics of Li-rich layered oxide cathode materials using the data of XPS and XRD spectra16Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective43Enhancing Structure–Property Relationships in Porous Materials through Transfer Learning and Cross-Material Few-Shot Learning44Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis68Optimal Surrogate Models for Predicting the Elastic Moduli of Metal–Organic Frameworks via Multiscale Features14A rule-free workflow for the automated generation of databases from scientific literature36Discovery Precision: An effective metric for evaluating performance of machine learning model for explorative materials discovery21Drug delivery system tailoring via metal-organic framework property prediction using machine learning: A disregarded approach20MatGD: Materials Graph Digitizer9Accelerating metal–organic framework discovery via synthesisability prediction: the MFD evaluation method for one-class classification models3Active learning of alchemical adsorption simulations; towards a universal adsorption model15Agent-based learning of materials datasets from the scientific literature51Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties28Image and data mining in reticular chemistry powered by GPT-4V68Integrating crystallographic and computational approaches to carbon-capture materials for the mitigation of climate change1Machine learning-based screening of Mn-PNP catalysts for the CO 2 reduction reaction using a region-wise ligand-encoded feature matrix7Metadata for Scientific Experiment Reporting: A Case Study in Metal-Organic Frameworks3Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications203Building robust metal-organic frameworks with premade ligands53Predicting the decomposition temperatures of metal-organic frameworks based on the pair distribution functions using a deep learning model4Informative Training Data for Efficient Property Prediction in Metal–Organic Frameworks by Active Learning33Machine Learning-Driven Ionic Liquids as Electrolytes for the Advancement of High-Voltage Dual-Ion Battery14Evaluation of Open-Source Large Language Models for Metal–Organic Frameworks Research37Oxide ceramics of A2M3O12 family with negative and close-to-zero thermal expansion coefficients: Machine learning-based modeling of functional characteristics12From Synthesis Conditions to UiO-66 Properties: Machine Learning Approach15Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties: application to NH3 adsorption1WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models2WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models1In Silico Generation of Chromium-Based MOFs with Abundant Active Sites for N2/CH4 Separation4The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture99Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo32Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes1Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery76Machine learning insights into predicting biogas separation in metal-organic frameworks15Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm26Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning1Machine Learning-Driven Discovery and Structure–Activity Relationship Analysis of Conductive Metal–Organic Frameworks34Computational Screening of Putative Catalyst Transition Metal Complexes as Guests in a Ga4L6–12 Nanocage1Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes10Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set88Design and optimization of spherical agglomeration process based on machine learning strategy11Isoreticular Curves: A Theory of Capillary Condensation To Model Water Sorption within Microporous Sorbents15Computational Screening of Putative Catalyst Transition Metal Complexes as Guests in a Ga4L612– Nanocage5Luminescent metal organic frameworks (LMOFs) and allied composites for the unveiling of organic environmental contaminants (explosive NACs, PAHs and EDCs) sensing through ‘Molecular Recognition’: A chronicle of recent penetration and future modelling52MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules5Machine‐learning‐assisted intelligent synthesis of UiO‐66(Ce): Balancing the trade‐off between structural defects and thermal stability for efficient hydrogenation of Dicyclopentadiene40From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks91Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO2 Conversion Reagents in Metal–Organic Frameworks6Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal–Organic Frameworks10MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules11Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales6Targeted metal–organic framework discovery goes digital: machine learning’s quest from algorithms to atom arrangements27Harnessing the Potential of Machine Learning to Optimize the Activity of Cu-Based Dual Atom Catalysts for CO2 Reduction Reaction20Machine Learning-Based Prediction of Proton Conductivity in Metal–Organic Frameworks20MOFs to Enhance Green NH3 Synthesis in Plasma Reactors: Hierarchical Computational Screening Enhanced by Iterative Machine Learning7Machine Learning-Driven Advances in Metal-Organic Framework Nanomaterials for Wastewater Treatment: Developments and Challenges27Machine learning accelerates high throughput design and screening of MOF mixed-matrix membranes towards He separation22CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening106Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction4Data‐Driven Innovation in Metal‐Organic Frameworks Photocatalysis: Bridging Gaps for CO 2 Capture and Conversion with FAIR Principles4AI-driven advances in metal–organic frameworks: from data to design and applications47Category-specific topological learning of metal–organic frameworks13Computational Exploration of Codoped Fe and Ru Single-Atom Catalysts for the Oxygen Reduction Reaction14Computer-aided metal–organic framework screening and design approaches toward efficient carbon capture processes9Digital design and discovery of biological metal–organic frameworks for gas signaling1MOSAEC-DB: a comprehensive database of experimental metal–organic frameworks with verified chemical accuracy suitable for molecular simulations44Polymeric membranes in carbon capture, utilization, and storage: current trends and future directions in decarbonization of industrial flue gas and climate change mitigation120Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications59Rising stars in AI use robotics and automation to accelerate their work0Computational Simulations of Metal-Organic Framework Structures Based on Second Building Units0Application of machine learning in adsorption energy storage using metal organic frameworks: A review17Harnessing Large Language Models to Collect and Analyze Metal–Organic Framework Property Data Set102Large language models for reticular chemistry133Modeling, screening, and techno-economic evaluation of metal–organic frameworks for boil-off gas capture during intercontinental transportation of LNG2Porous Organic Framework-Based Materials (MOFs, COFs and HOFs) for Lithium-/Sodium-/Potassium-/Zinc-/Aluminum-/Calcium-Ion Batteries: A Review91Conductive Metal–Organic Frameworks and Their Electrocatalysis Applications81Unlocking potential and challenges of MOFs and COFs based energy materials for CO2 reduction and H2 production51Using experimental data in computationally guided rational design of inorganic materials with machine learning7Accelerating Band Gap Prediction and High-Throughput Screening of Covalent Organic Frameworks Based on Transfer Learning4Identifying MOFs for electrochemical energy storage via density functional theory and machine learning51Discovering Ultra-Stable Metal–Organic Frameworks for CO2 Capture from A Wet Flue Gas: Integrating Machine Learning and Molecular Simulation24From pages to patterns: Towards extracting catalytic knowledge from structure and text for transition-metal complexes and metal-organic frameworks6Retrospective Review on Reticular Materials: Facts and Figures Over the Last 30 Years22Toward Molecular Simulation Guided Design of Next-Generation Membranes: Challenges and Opportunities14Identifying Key Factors Influencing Advanced Hydrogen Evolution Reaction Catalysts5Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity13Machine Learning-Guided Conductivity Prediction in 2D Organic Metal Chalcogenides for Accelerated Electromagnetic Wave Absorber Design8A Multitask Learning Model for Predicting Various Types of Metal–Organic Framework Stability7Data-Driven Insights on the Impact of Functionalization on Metal–Organic Framework Free Energies5Use of Large Language Models for Extracting and Analyzing Data from Heterogeneous Catalysis Literature1833 Unresolved Questions in Nanoscience and Nanotechnology58Accelerating the discovery and optimization of metal-organic framework materials via machine learning64

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