Estimation of activation energies within heterogeneous catalytic reactions is performed using machine learning and catalysts dataset. In particular, descriptors for determining activation energy are revealed within the 788 activation energy dataset. With the implementation of machine learning and chosen descriptors, activation energy can be instantly predicted with over 90% accuracy during cross‐validation. Thus, rapid estimation of activation energies within heterogeneous catalytic reactions can be made achievable via machine learning, leading toward the acceleration of catalysts design and characterization. © 2018 Wiley Periodicals, Inc.

中文翻译：

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通过机器学习快速估算多相催化反应中的活化能

使用机器学习和催化剂数据集来估计多相催化反应中的活化能。特别是，在 788 活化能数据集中揭示了用于确定活化能的描述符。通过实施机器学习和选择的描述符，可以在交叉验证期间以超过 90% 的准确率立即预测激活能量。因此，可以通过机器学习实现对多相催化反应中活化能的快速估计，从而加速催化剂的设计和表征。© 2018 威利期刊公司。