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RESEARCH PAPER · Catalysts · 2021

Molecular Dynamics and Machine Learning in Catalysts

Wenxiang Liu, Yang Zhu, Yongqiang Wu, Cen Chen, Yang Hong, Yanan Yue, Jingchao Zhang, Bo Hou

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Molecular Dynamics and Machine Learning in CatalystsOriginalApplications of machine learning in computational nanotechnology13Machine learning for design principles for single atom catalysts towards electrochemical reactions116The field of applied mechanics: historical outlook from past to present and its implications for future applications1Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective6Prediction and Construction of Energetic Materials Based on Machine Learning Methods53Theory, Modeling and Computational Aspects Regarding the Mechanisms of Activation of Photocatalysts0Recent advances of computational studies on bioethanol to light olefin reactions using zeolite and metal oxide catalysts16Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions27Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling11Early Prediction of Ion Transport Properties in Solid Polymer Electrolytes Using Machine Learning and System Behavior-Based Descriptors of Molecular Dynamics Simulations28A principal component analysis assisted machine learning modeling and validation of methanol formation over Cu-based catalysts in direct CO2 hydrogenation46Unlocking New Insights for Electrocatalyst Design: A Unique Data Science Workflow Leveraging Internet-Sourced Big Data32HIDROGENAÇÃO DE CO2 PARA METANOL: O PAPEL DAS VACÂNCIAS DE OXIGÊNIO NA SÍNTESE DE METANOL EMPREGANDO OS CATALISADORES DE CU/ZNO/AL E AS MISTURAS FÍSICAS A BASE DE IN2O30Combining genetic algorithm and deep learning to optimize a chemical kinetic mechanism of ammonia under high pressure17Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses37From Organic Fragments to Photoswitchable Catalysts: The OFF–ON Structural Repository for Transferable Kernel-Based Potentials3A novel dynamic simulation method for investigating the hygroscopicity of Ammonium perchlorate4Surface reaction and lattice oxygen transfer in chemical looping oxidative coupling of methane: Molecular dynamics simulations4Low-dimensional N-heterocyclic carbenes nanomaterials: Promising supports of single atom catalysts4Molecular modeling applied to corrosion inhibition: a critical review74Molecular Dynamics Simulations for Electrocatalytic CO 2 Reduction: Bridging Macroscopic Experimental Observations and Microscopic Explanatory Mechanisms49Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds2Advances in Catalysts for Hydrogen Production: A Comprehensive Review of Materials and Mechanisms85Catalytic Properties and Structural Optimization of Solid Transesterification Catalysts to Enhance the Efficiency of Biodiesel Synthesis32Applications of molecular dynamics in nanomaterial design and characterization - A review44Machine Learning-Assisted Catalysts for Advanced Oxidation Processes: Progress, Challenges, and Prospects35Advancing CO2 Conversion with Cu‐LDHs: A Review of Computational and Experimental Studies7Models of thermal motion in small-molecule crystallography5Advances and Challenges in Computational Simulation of Calcium Phosphate Nucleation2A synthesis-structure-catalytic activity map for CO oxidation on dealloyed Au-Ag nanoparticles derived using multiscale simulations2Infrared Photovoltaic–Battery Hybrid Systems Enabled by Colloidal Quantum Dots1Predictive modeling and feature attribution of CO₂ adsorption on LDH-derived materials using machine learning approach7Advanced Bimetallic Catalysts for Enhanced Water Splitting Efficiency0Computational techniques to model nanomaterials0Advanced Oxidation Techniques and Hybrid Approaches for Microplastic Degradation: A Comprehensive Review7Molecular Dynamics Simulations and Current Trends in Designing New Antivirals Targeting Proteases: A Review from 2018 to 20250A novel synergistic insight into the experimentally observed antifungal potential of zinc oxide nanostructures against candida albicans1AI-Driven Acceleration of Nanomaterials Development: Advanced Computational Tools and Techniques0Rational and Multidimensional Optimization of Nanozyme Catalytic Performance6Accelerating Sustainable Energy Catalysis through Machine Learning: High-Throughput Screening and Machine Learning-Based Potential Energy Surface2On the surface orientation effects of hydrogen mobility on Pt and Ni using atomistic modelling1Review: From research progress to real-world marine applications of direct seawater electrolysis for hydrogen production1A review of computational modeling for platinum-based alloys: From DFT to AI0A review of modern design methods and multi-scale coupling simulation strategies for high-entropy alloys0Machine Learning Potentials for Biomaterials: Applications and Perspectives0AtomFlow: Accelerating GNN-Based Molecular Dynamics with ab initio Accuracy on Fugaku Supercomputer0

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  2. Machine learning for design principles for single atom catalysts towards electrochemical reactions

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  3. The field of applied mechanics: historical outlook from past to present and its implications for future applications

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  4. Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective

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  5. Prediction and Construction of Energetic Materials Based on Machine Learning Methods

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  6. Theory, Modeling and Computational Aspects Regarding the Mechanisms of Activation of Photocatalysts

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  7. Recent advances of computational studies on bioethanol to light olefin reactions using zeolite and metal oxide catalysts

    Chemical Engineering Science · 2023-02-02

    Yu Fan, Wei Xia, Chao Ma, Yaxin Huang, Shuangshuang Li, Xue Wang, Qian Chen, Kun Long Chen, Dong Liu

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  8. Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions

    Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2023-05-21

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  9. Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

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  10. Early Prediction of Ion Transport Properties in Solid Polymer Electrolytes Using Machine Learning and System Behavior-Based Descriptors of Molecular Dynamics Simulations

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  11. A principal component analysis assisted machine learning modeling and validation of methanol formation over Cu-based catalysts in direct CO2 hydrogenation

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  12. Unlocking New Insights for Electrocatalyst Design: A Unique Data Science Workflow Leveraging Internet-Sourced Big Data

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  13. HIDROGENAÇÃO DE CO2 PARA METANOL: O PAPEL DAS VACÂNCIAS DE OXIGÊNIO NA SÍNTESE DE METANOL EMPREGANDO OS CATALISADORES DE CU/ZNO/AL E AS MISTURAS FÍSICAS A BASE DE IN2O3

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  14. Combining genetic algorithm and deep learning to optimize a chemical kinetic mechanism of ammonia under high pressure

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  15. Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses

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  16. From Organic Fragments to Photoswitchable Catalysts: The OFF–ON Structural Repository for Transferable Kernel-Based Potentials

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  17. A novel dynamic simulation method for investigating the hygroscopicity of Ammonium perchlorate

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  18. Surface reaction and lattice oxygen transfer in chemical looping oxidative coupling of methane: Molecular dynamics simulations

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  19. Low-dimensional N-heterocyclic carbenes nanomaterials: Promising supports of single atom catalysts

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  20. Molecular modeling applied to corrosion inhibition: a critical review

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  21. Molecular Dynamics Simulations for Electrocatalytic CO 2 Reduction: Bridging Macroscopic Experimental Observations and Microscopic Explanatory Mechanisms

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  22. Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds

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