Deep Learning of Activation Energies.,The Journal of Physical Chemistry Letters

当前位置： X-MOL 学术 › J. Phys. Chem. Lett. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Deep Learning of Activation Energies.
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2020-04-01 , DOI: 10.1021/acs.jpclett.0c00500
Colin A Grambow ₁ , Lagnajit Pattanaik ₁ , William H Green ₁

Affiliation

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 accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and the development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future.

中文翻译：

活化能的深度学习。

由于缺乏可用的训练数据，反应特性（例如活化能）的定量预测受到限制。这种预测对于计算机辅助反应机理生成和有机合成规划将很有用。我们开发了一个无模板深度学习模型来预测给定反应物和产物图的活化能，并在气相量子化学反应的新的、多样化的数据集上训练模型。我们证明我们的模型可以实现准确的预测，并符合对化学反应性的直观理解。随着定量化学反应数据的不断生成以及利用这些数据的方法的发展，我们预计在不久的将来将出现更多的反应预测方法。

更新日期：2020-04-24

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11