首页|Chalmers University of Technology Reports Findings in Machine Learning (Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics)
Chalmers University of Technology Reports Findings in Machine Learning (Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics)
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New research on Machine Learning is the subject of a report. According to news reporting originating from Goteborg, Sweden, by NewsRx correspondents, research stated, “Altering chemical reactivity and material structure in confined optical environments is on the rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive. This originates mostly from the fact that accurately predicting vibrational and reactive dynamics for soluted ensembles of realistic molecules is no small endeavor, and adding (collective) strong light-matter interaction does not simplify matters.” Our news editors obtained a quote from the research from the Chalmers University of Technology, “Here, we establish a framework based on a combination of machine learning (ML) models, trained using density-functional theory calculations and molecular dynamics to accelerate such simulations. We then apply this approach to evaluate strong coupling, changes in reaction rate constant, and their influence on enthalpy and entropy for the deprotection reaction of 1-phenyl-2-trimethylsilylacetylene, which has been studied previously both experimentally and using simulations. While we find qualitative agreement with critical experimental observations, especially with regard to the changes in kinetics, we also find differences in comparison with previous theoretical predictions. The features for which the ML-accelerated and simulations agree show the experimentally estimated kinetic behavior. Conflicting features indicate that a contribution of dynamic electronic polarization to the reaction process is more relevant than currently believed.”
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