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RESEARCH PAPER · Digital Discovery · 2023

Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations

Nickolas Gantzler, Aryan Deshwal, Janardhan Rao Doppa, Cory M. Simon

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Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separationsOriginalRole of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap7Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties28Accelerating In Silico Discovery of Metal–Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning27Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties: application to NH3 adsorption1Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior15Multi-fidelity Bayesian Optimization for Efficiently Sampling the Design Space of Functionalized Nanoporous Materials2Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization3Accelerating Discovery of Mechanically Stable Metal–Organic Frameworks for Vinylidene Fluoride Storage by Active Learning6MOFs to Enhance Green NH3 Synthesis in Plasma Reactors: Hierarchical Computational Screening Enhanced by Iterative Machine Learning7A case study in the development of improved promoted Pt catalysts for propane dehydrogenation through Bayesian optimization with uncertainty quantification14A Multi-Fidelity Learning Framework for High-Entropy Alloy Design with Sparse DFT and Abundant CALPHAD Data0Atlas: a brain for self-driving laboratories50Computer-aided metal–organic framework screening and design approaches toward efficient carbon capture processes9Machine-learning-guided design of electroanalytical pulse waveforms15Multi-Fidelity Learning in High-Throughput Materials Screening: A Review of Surrogate Strategies and Their Broken Promises0MULTI-OBJECTIVE BAYESIAN METHOD FOR ENHANCED HDH SYSTEM DESIGN0Navigating chemical space: multi-level Bayesian optimization with hierarchical coarse-graining2Navigating materials design spaces with efficient Bayesian optimization: a case study in functionalized nanoporous materials0Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules29High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks43Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research54Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites6Best practices for multi-fidelity Bayesian optimization in materials and molecular research30Guidelines for multi-fidelity Bayesian optimization of molecules and materials1Machine learning optimized adsorption heat transformations from adsorbents screening to enhanced performances38Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective14Metal-organic frameworks with S-shaped channel triangular cage window for efficient capture of trace krypton3A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation1From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons9Active Learning for Transport Property Prediction in CO 2 –Hydrocarbon Systems: A Multifidelity Approach Integrating Molecular Dynamics and Experiments1Intelligent screening of porous materials: A review of active-learning approaches in MOF research6Machine learning to design metal–organic frameworks: progress and challenges from a data efficiency perspective22BayesCAMPD : Data‐efficient and closed‐loop integrated molecular and process design using Bayesian optimization1Adaptive Allocation of Monte Carlo Samples for Efficient, Multifidelity Computational Screening of Metal–Organic Frameworks3Graph neural network-based multi-objective Bayesian optimization for enhanced screening of metal–organic frameworks with optimal separation performance7Toward self-driving laboratory 2.0 for chemistry and materials discovery15The rise of generative AI for metal-organic framework design and synthesis21PPO-GPR: A Custom Proximal Policy Optimization Tool for Active Reinforcement Learning0Rational Creation of the Second Xe-Matched Adsorption Site in a Covalent Organic Framework via a Digging-Patching Strategy for Simultaneously Boosting Xe Uptake and Xe/Kr Selectivity3A Framework for the Bayesian Calibration of Complex and Data-Scarce Models in Applied Sciences0AI‐driven discovery of feasible 3D printing configurations for metal alloys0

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  1. Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap

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  2. Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties

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    Tsung-Wei Liu, Quan Nguyen, Adji Bousso Dieng, Diego A. Gómez‐Gualdrón

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  3. 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

    ACS Applied Materials & Interfaces · 2024-01-30

    Varad Daoo, Jayant Kumar Singh

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  4. Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties: application to NH3 adsorption

    ChemRxiv · 2024-04-16

    Tsung-Wei Liu, Quan Nguyen, Adji Bousso Dieng, Diego A. Gómez‐Gualdrón

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  5. Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior

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  6. Multi-fidelity Bayesian Optimization for Efficiently Sampling the Design Space of Functionalized Nanoporous Materials

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

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

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    Yifei Yue, Athulya Surendran Palakkal, Saad Aldin Mohamed, Jianwen Jiang

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

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

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    Unni Kurumbail, Harsh R. Darji, Matias Alvear, Siying Chen, Ive Hermans

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  11. A Multi-Fidelity Learning Framework for High-Entropy Alloy Design with Sparse DFT and Abundant CALPHAD Data

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  12. Atlas: a brain for self-driving laboratories

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  13. Computer-aided metal–organic framework screening and design approaches toward efficient carbon capture processes

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  14. Machine-learning-guided design of electroanalytical pulse waveforms

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  15. Multi-Fidelity Learning in High-Throughput Materials Screening: A Review of Surrogate Strategies and Their Broken Promises

    Journal of Computational and Data-Driven Materials Engineering · 2025-01-01

    Anna Svensson, Erik Lindberg

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  16. MULTI-OBJECTIVE BAYESIAN METHOD FOR ENHANCED HDH SYSTEM DESIGN

    Proceeding of Second Thermal and Fluids Engineering Conference · 2025-01-01

    Mahyar Abedi, Mohammed Abbas Elhashimi Khalifa, Bahman Abbasi

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  17. Navigating chemical space: multi-level Bayesian optimization with hierarchical coarse-graining

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  18. Navigating materials design spaces with efficient Bayesian optimization: a case study in functionalized nanoporous materials

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

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    Matthew A. McDonald, Brent A. Koscher, Richard B. Canty, Jason Zhang, Angelina Ning, Klavs F. Jensen

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

    npj Computational Materials · 2025-05-02

    Haoyi Tan, Yukun Teng, Guangcun Shan

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

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

    Advanced Intelligent Discovery · 2025-07-09

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  23. Best practices for multi-fidelity Bayesian optimization in materials and molecular research

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  24. Guidelines for multi-fidelity Bayesian optimization of molecules and materials

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

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

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

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