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Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules

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Accelerating Discovery of Mechanically Stable Metal–Organic Frameworks for Vinylidene Fluoride Storage by Active Learning

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Machine learning optimized adsorption heat transformations from adsorbents screening to enhanced performances

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Metal-organic frameworks with S-shaped channel triangular cage window for efficient capture of trace krypton

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Adaptive Allocation of Monte Carlo Samples for Efficient, Multifidelity Computational Screening of Metal–Organic Frameworks

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Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

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Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research

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BayesCAMPD: Data‐efficient and closed‐loop integrated molecular and process design using Bayesian optimization

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High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks

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A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

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From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons

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Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective

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Guidelines for multi-fidelity Bayesian optimization of molecules and materials

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Intelligent screening of porous materials: A review of active-learning approaches in MOF research

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MOFs to Enhance Green NH3 Synthesis in Plasma Reactors: Hierarchical Computational Screening Enhanced by Iterative Machine Learning

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Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

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A case study in the development of improved promoted Pt catalysts for propane dehydrogenation through Bayesian optimization with uncertainty quantification

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Accelerating In Silico Discovery of Metal–Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning

Varad Daoo and Jayant K. Singh

ACS Appl. Mater. Interfaces, 2024, 16, 6971
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Active Learning for Transport Property Prediction in CO2–Hydrocarbon Systems: A Multifidelity Approach Integrating Molecular Dynamics and Experiments

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