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Showing 1 to 4 of 4 for “"computational catalysis"”.
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Machine Learning Force Fields for Modelling Reactions at Complex Interfaces
Computational modelling at the atomic scale has traditionally been constrained by a stark trade-off between accuracy and computational cost. Recently, machine learning (ML) architectures have been trained on highly accurate quantum mechanical calculations to sidestep this …
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Accelerating Catalyst Discovery via Ab Initio Machine Learning
… algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory …
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Understanding Interfacial Kinetics of Catalytic Carbon Dioxide Transformations from Multiscale Simulations
… (Chapter 6). Although recent advances in the computational catalysis field have significantly push forward the understanding of the chemistry nature of heterogeneous catalysis, the gap between theory and experiment remains far beyond bridged due to the complexity nature of the problem in a …
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Atomic-level Insights Into Atomically Dispersed Metal Catalysts
… have demonstrated promise in heterogeneous catalysis with both nearly 100% utilization efficiency of precious metals and high intrinsic activity of metal sites. As an emergent type of atomically dispersed metal catalysts, single-atom catalysts (SACs) have been extensively investigated with …