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RESEARCH PAPER · Angewandte Chemie International Edition · 2022

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**

Yi Luo, Saientan Bag, Orysia T. Zaremba, Adrian Cierpka, Jacopo Andreo, Stefan Wuttke, Pascal Friederich, Manuel M. Tsotsalas

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MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**OriginalAccessing accelerated molecular diffusion by nanopore alignment in a MOF thin film7An Interpretable Machine Learning Model to Guide the Design of Organic Phosphorus-Containing Flame Retardants for Improving the Limiting Oxygen Index of Epoxy Resins0From computational high-throughput screenings to the lab: taking metal–organic frameworks out of the computer28Metalated covalent organic frameworks: from synthetic strategies to diverse applications326MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis21An ecosystem for digital reticular chemistry4Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X77Controlled Synthesis of Multicolor Carbon Dots Assisted by Machine Learning71VARIETY OF METAL-ORGANIC FRAMEWORKS BASED ON CADMIUM(II) AND BITHIOPHENEDICARBOXYLIC ACID2Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins76Graph neural networks for materials science and chemistry860A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation24Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach16Recent advances and challenges in experiment-oriented polymer informatics42Fully Automated Optimization of Robot‐Based MOF Thin Film Growth via Machine Learning Approaches45Implementation of Data Mining Techniques in Healthcare3A database to select affordable MOFs for volumetric hydrogen cryoadsorption considering the cost of their linkers9Extracting higher-conductivity designs for solid polymer electrolytes by quantum-inspired annealing11Synthesis, characterization & catalysis of ITQ 2D metal–organic frameworks and spectroscopic & photodynamic properties of their composites with organic dyes5Unveiling the synthesis patterns of nanomaterials: a text mining and meta-analysis approach with ZIF-8 as a case study9Utilizing machine learning to optimize metal–organic framework-derived polymer membranes for gas separation24What is missing in autonomous discovery: open challenges for the community44Machine Learning and Digital Manufacturing Approaches for Solid‐State Materials Development5Overview of AI in the Understanding and Design of Nanoporous Materials2Sequential Sol‐Gel Self‐Assembly and Nonclassical Gel‐Crystal Transformation of the Metal‐Organic Framework Gel20Layer-by-layer assembly of metal-organic framework thin films: Fabrication and advanced applications64Quid Pro Flow60Untitled work1An Ecosystem for Digital Reticular Chemistry52Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217CHEMICAL SPACE ANALYSIS AND PROPERTY PREDICTION FOR CARBON CAPTURE AMINE MOLECULES0DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining86Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use6Artificial intelligence assisted with designing metal‐organic frameworks (MOFs)17High-throughput and machine learning approaches for the discovery of metal organic frameworks18Beyond Solvothermal: Alternative Synthetic Methods for Covalent Organic Frameworks96Beyond Solvothermal: Alternative Synthetic Methods for Covalent Organic Frameworks10Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33Review: When worlds collide – poultry modeling in the ‘Big Data’ era21Metal-organic framework based nanomaterials: An advanced review of their synthesis and energy storage applications10A new far-UVC class tricarboxylate-Bi-based metal organic frameworks grown by hydrothermal method1Unified roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and their Impact on Particle Size and Morphology2Prospects of artificial intelligence in the development of sustainable separation processes23ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis536The Impact of Digitalized Data Management on Materials Systems Workflows5Recent 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 capture48A GPT‐4 Reticular Chemist for Guiding MOF Discovery**162A GPT‐4 Reticular Chemist for Guiding MOF Discovery**13MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal–organic frameworks utilizing graph convolutional networks16Application of Fe-MOFs in Photodegradation and Removal of Air and Water Pollutants: A Review38Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid21Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective43ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs168Biomedical Metal–Organic Framework Materials: Perspectives and Challenges188Fabrication of Oriented Polycrystalline MOF Superstructures86High-aspect-ratio nanostructured hydroxyapatite: towards new functionalities for a classical material79Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis68Accelerated Discovery of Metal–Organic Frameworks for CO 2 Capture by Artificial Intelligence48Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)23Chemical space analysis and property prediction for carbon capture solvent molecules4Digital chemistry: navigating the confluence of computation and experimentation – definition, status quo, and future perspective11Image and data mining in reticular chemistry powered by GPT-4V68Nanoarchitectonics of Metal–Organic Frameworks (MOFs) for energy and sensing applications3Realizing altermagnetism in two-dimensional metal–organic framework semiconductors with electric-field-controlled anisotropic spin current43Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and Mofs Using Machine Learning1Utilizing Data Mining for the Synthesis of Functionalized Tungsten Oxide with Enhanced Oxygen Vacancies for Highly Sensitive Detection of Triethylamine21Reviewing direct air capture startups and emerging technologies42Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal–Organic Frameworks: Application to the Adsorptive Separation of C3 Components6MOFs-Based Materials for Solid-State Hydrogen Storage: Strategies and Perspectives155Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design8Evaluation of Open-Source Large Language Models for Metal–Organic Frameworks Research37Metal-organic framework-based biosensing platforms for diagnosis of bacteria-induced infectious diseases36Optimizing the Synthesis Parameters of Double Perovskites with Machine Learning Using a Multioutput Regression Model10Embracing data science in catalysis research117Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks46The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture99State-of-the-art advancements in metal−organic framework nanoarchitectures for catalytic applications36Combining 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 processes42Enhancing the Quality of MOF Thin Films for Device Integration Through Machine Learning: A Case Study on HKUST‐1 SURMOF Optimization20Unifying mixed gas adsorption in molecular sieve membranes and MOFs using machine learning15Current update and prospects in the development of conductive metal-organic framework electrodes for lithium-based batteries13Computer-Aided Design of Covalent Organic Frameworks for SF6 Capture: The Combination of High-Throughput Screening and Machine Learning26Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF4 from CH4 and N26Unveiling the limitless potential: Exploring metal–organic frameworks (MOFs)/MXene based construction materials25Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning24MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules5Transitioning metal–organic frameworks from the laboratory to market through applied research245Machine Learning Optimizing Enzyme/ZIF Biocomposites for Enhanced Encapsulation Efficiency and Bioactivity30Self-Driving Laboratories for Chemistry and Materials Science674Molecular simulations and deep neural networks‐based interpretable machine learning modelling of reverse adsorptive MOFs for ethane/ethylene separation3Machine learning to optimize nonlinear conductive performance of composites for self‐adaptive electromagnetic shielding6Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74Osteoinductive biomaterials: Machine learning for prediction and interpretation25Rise of Metal–Organic Frameworks: From Synthesis to E-Skin and Artificial Intelligence34How the planarity of two dimensional covalent organic frameworks affect gas adsorption and diffusion: A data mining, simulation, and experiment united study5ROBERT : Bridging the Gap Between Machine Learning and Chemistry49Routes to Bidirectional Cathodes for Reversible Aprotic Alkali Metal–CO 2 Batteries44When Metal Nanoclusters Meet Smart Synthesis31Effect of crystallization conditions on the composition and structure of {ZnCa} metal-organic coordination polymers with isophthalate anions4MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules11Automated synthesis and processing of functional nanomaterials: Advances and perspectives27Machine learning applications in nanomaterials: Recent advances and future perspectives94Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales6Theoretical Calculation–Driven Metal–Organic Framework Nanozymes: Catalytic Mechanisms and Applications9Metal–Organic Framework and Covalent–Organic Framework‐Based Aerogels: Synthesis, Functionality, and Applications83Solution Processable Metal–Organic Frameworks: Synthesis Strategy and Applications34Targeted metal–organic framework discovery goes digital: machine learning’s quest from algorithms to atom arrangements27Host-guest interactions in the confined spaces of metal–organic frameworks: Design principles, characterizations, and applications51Recent developments on the design of advanced porous materials based on Metal Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), and their combinations into composites as sorbents in sample preparation and chromatographic applications0Interfacial Microenvironment-Regulated Coordinate Structures Dictate the Metal–Organic Framework Facet Orientation toward Efficient CO2 Cycloaddition8Cost-Aware Bayesian Optimization for Self-Driven Condition Screening of Flow Electrosynthesis15Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions272Weaving the gates of life: Pioneering a new era in oral gene delivery with metal-organic frameworks17High‐Entropy Metal–Organic Frameworks and Their Derivatives: Advances in Design, Synthesis, and Applications for Catalysis and Energy Storage58Comprehensive overview of machine learning applications in MOFs: from modeling processes to latest applications and design classifications26Incorporation of Ligand Charge and Metal Oxidation State Considerations into the Computational Solvent Removal and Activation of Experimental Crystal Structures Preceding Molecular Simulation4Data‐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 frameworks13Covalent organic and metal organic frameworks based single atom catalysts for valorisation of CO 2 to value added chemicals37Extraction of chemical synthesis information using the World Avatar4Multi-competitor directed defect engineering in UiO-66: achieving hierarchical porosity and unsaturated sites for high-efficiency fluoroquinolone remediation4Natural language access point to digital metal–organic polyhedra chemistry in The World Avatar10Optimizing the Design of Metal Organic Frameworks for Drug Delivery Applications Through Machine Learning Integration0Text mining in MOF research: from manual curation to large language model-based automation11Machine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor3Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications59Integrating Enzymes with Reticular Frameworks To Govern Biocatalysis0Integrating Enzymes with Reticular Frameworks To Govern Biocatalysis352 H NMR as a Practical Tool for Following MOF Formation: A Case Study of UiO‐6682H NMR as a Practical Tool for Following MOF Formation: A Case Study of UiO‐662Inverse Design of 2D Altermagnetic Metal–Organic Framework Monolayers from Hückel Theory of Nonbonding Molecular Orbitals29MOF membranes for gas separations141An Intelligent Prediction Model for the Synthesis Conditions of Metal–Organic Frameworks Utilizing Artificial Neural Networks Enhanced by Genetic Algorithm Optimization12Harnessing Large Language Models to Collect and Analyze Metal–Organic Framework Property Data Set102Large language models for reticular chemistry133Validation and Mechanistic Studies of the Headspace Effect in MOF Synthesis2Validation and Mechanistic Studies of the Headspace Effect in MOF Synthesis8Machine learning-assisted three-dimensional fluorescence for heavy metal multi-sensing23Porous Organic Framework-Based Materials (MOFs, COFs and HOFs) for Lithium-/Sodium-/Potassium-/Zinc-/Aluminum-/Calcium-Ion Batteries: A Review91Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review41Conductive Metal–Organic Frameworks and Their Electrocatalysis Applications81Federated Transfer Learning for Early Detection of Multi-Organ Failure: A Scalable Predictive Healthcare Framework2Advancements in Functionalizable Metal‐Organic Frameworks for Flexible Sensing Electronics26Machine Learning‐Assisted Exploration of Chemical Space of MOF‐5 Analogs for Enhanced C 2 H 6 /C 2 H 4 Separation29Machine Learning‐Assisted Exploration of Chemical Space of MOF‐5 Analogs for Enhanced C2H6/C2H4 Separation0

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  45. ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

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  47. Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

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  48. Integrating stability metrics with high-throughput computational screening of metal–organic frameworks for CO2 capture

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  49. A GPT‐4 Reticular Chemist for Guiding MOF Discovery**

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  50. A GPT‐4 Reticular Chemist for Guiding MOF Discovery**

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