Ontology highlight
ABSTRACT:
SUBMITTER: Grambow CA
PROVIDER: S-EPMC7311089 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Grambow Colin A CA Pattanaik Lagnajit L Green William H WH
The journal of physical chemistry letters 20200401 8
Quantitative predictions 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 the 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 accur ...[more]