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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2022

Mining Insights on Metal–Organic Framework Synthesis from Scientific Literature Texts

Hyunsoo Park, Yeonghun Kang, Wonyoung Choe, Jihan Kim

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Mining Insights on Metal–Organic Framework Synthesis from Scientific Literature TextsOriginalAudacity 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 frameworks18Constructing strategies for hierarchically porous MOFs with different pore sizes and applications in adsorption and catalysis95From computational high-throughput screenings to the lab: taking metal–organic frameworks out of the computer28Computational discovery of Metal–Organic Frameworks for sustainable energy systems: Open challenges9Current trends in the synthesis, characterization and application of metal-organic frameworks46Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF31Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions18Machine Learning and Digital Manufacturing Approaches for Solid‐State Materials Development5Overview of AI in the Understanding and Design of Nanoporous Materials2Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining86Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis536A GPT‐4 Reticular Chemist for Guiding MOF Discovery**162A GPT‐4 Reticular Chemist for Guiding MOF Discovery**13Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature7Host–guest interactions in framework materials: Insight from modeling28Advancing thermoelectric materials discovery through semi-supervised learning and high-throughput calculations23A new methodology for the prediction of optimal conditions for dyes' electrochemical removal; Application of copula function, machine learning, deep learning, and multi-objective optimization21When LLM Meets Material Science: An Investigation on MOF Synthesis Labeling2Accelerated Discovery of Metal–Organic Frameworks for CO 2 Capture by Artificial Intelligence48MatGD: Materials Graph Digitizer9Bi- and trimetallic MOFs and their MOF-derived nanocarbons in electrocatalytic water splitting processes4Image and data mining in reticular chemistry powered by GPT-4V68Knowledge Graph Question Answering for Materials Science (KGQA4MAT)10Metadata for Scientific Experiment Reporting: A Case Study in Metal-Organic Frameworks3Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications203Data-driven exploration of terbium-doped tungsten oxide for Ultra-Precise detection of 3H-2B: Implications for gas sensor applications13Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery76Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior15From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks91Knowledge Graph for Solubility Big Data: Construction and Applications2Targeted metal–organic framework discovery goes digital: machine learning’s quest from algorithms to atom arrangements27Machine Learning-Based Prediction of Proton Conductivity in Metal–Organic Frameworks20Finding environmental-friendly chemical synthesis with AI and high-throughput robotics12Cyclic stress-strain behavior and microstructural features in copper-Cu50Zr50 metallic glass core-shell structures: Molecular dynamics and deep machine learning predictions2Comprehensive 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 Simulation4Extraction of chemical synthesis information using the World Avatar4Text mining in MOF research: from manual curation to large language model-based automation11Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications59Harnessing Large Language Models to Collect and Analyze Metal–Organic Framework Property Data Set102Large language models for reticular chemistry133How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?9Using experimental data in computationally guided rational design of inorganic materials with machine learning7From pages to patterns: Towards extracting catalytic knowledge from structure and text for transition-metal complexes and metal-organic frameworks6High Structural Error Rates in “Computation-Ready” MOF Databases Discovered by Checking Metal Oxidation States33Building an end-to-end battery recipe knowledge base via transformer-based text mining10A one-shot automated framework based on large language model and AutoML: Accelerating the design of porous carbon materials and carbon capture optimization5Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research54Recent advances in computational screening of metal-organic frameworks for carbon capture6Identification of Metal–Organic Frameworks for near Practical Energy Limit CO 2 Capture from Wet Flue Gases: An Integrated Atomistic and Process Simulation Screening of Experimental MOFs15Transfer learning-based modeling of synthesis-particle size relationships in metal-organic frameworks3Next-generation MOFs for atmospheric water harvesting: The role of machine learning techniques22Predicting Substrate Reactivity in Oxidative Homocoupling of Phenols Using Positive and Unlabeled Machine Learning4The evolution of MOF discovery for CO2 capture: From high-throughput screening to AI design and automated laboratories9Machine learning accelerates the development of metal–organic frameworks0Machine learning to design metal–organic frameworks: progress and challenges from a data efficiency perspective22Multiobjective Optimization of Metal–Organic Framework Structural Properties and Synthesis Costs through Machine Learning4MaterialBrain: High-Performance Material Synthesis Extraction via Human–AI-Curated Few-Shot Large Language Models2Large language models for porous materials: from text mining to autonomous laboratory6Packing efficiency governs CO 2 /H 2 selectivity in machine-learning-screened metal–organic frameworks0RETRACTED: Reshaping MOFs synthesis conditions mining with a dynamic multi-agents framework of large language model5Use of Machine Learning and Large Language Models in Chemical Information Extraction0Reimagining metal-organic framework discovery: Integrating experiment, computation, and artificial intelligence6Quantitative analysis of operating conditions and performance trends in CO2 capture from large-scale literature0Developing an AI Course for Synthetic Chemistry Students1Data Management and Analysis of Metal–Organic Framework Synthesis Using Data Models1Toward clinically viable fluorescent MOFs: Green synthesis and emerging bioimaging applications0Metal‐Organic Frameworks‐Derived Carbon‐Based Materials for Energy Storage and Conversion0

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