Deep Learning of Activation Energies.

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ID: 101759
2020
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Abstract
Quantitative prediction of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model to predict activation energy given reactant and product graphs and train the model on a new, diverse data set of gas-phase quantum chemistry reactions. We demonstrate that our model achieves accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future.
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grambow2020deepthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Grambow, Colin A;Pattanaik, Lagnajit;Green, William H;
Journal The journal of physical chemistry letters
Year 2020
DOI
10.1021/acs.jpclett.0c00500
URL
Keywords
Antimicrobial activity quantum dots chemosensor cds cell image phosphate ion sensing

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