(443h) Generalized Brønsted Evans Polanyi Relationships for Metal Surfaces from Machine Learning
- Conference: AIChE Annual Meeting
- Year: 2019
- Proceeding: 2019 AIChE Annual Meeting
- Group: Topical Conference: Applications of Data Science to Molecules and Materials
- Time: Wednesday, November 13, 2019 - 10:06am-10:24am
Here we develop generalized BEPs for a diverse set of reactions on metal surfaces. We focus on 788 reactions from the CatApp database , a database that contains reaction- and activation energies for reactions on monometallic surfaces calculated using the DACAPO code and using the RPBE density functional. Our analysis builds on typical BEPs, which only use the reaction energy as a descriptor. However, to improve accuracy we introduce a set of descriptors for the type of reaction encountered derived from scaling relationships  and add an additional descriptor for the catalyst surface. In a second step we use three different machine-learning approaches for inter- and extrapolation. We find a significant increase in accuracy for the prediction of activation energies and our best approach leads to errors that are comparable to errors typically associated with DFT calculations. In further analysis, we find that MAE values are similar for different types of reactions and we study the impact of training set size on the performance of the approach. Finally we confirm that the reaction energy is the most important descriptor, but all the other introduced parameters collectively contribute significantly to the improved description.
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