A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the cost of calculating the adsorption energies by DFT for a large number of reaction intermediates...

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Bibliographic Details
Published in:arXiv.org 2019-10
Main Authors: Chowdhury, Asif J, Yang, Wenqiang, Abdelfatah, Kareem E, Zare, Mehdi, Heyden, Andreas, Terejanu, Gabriel
Format: Article
Language:English
Subjects:
Adsorption
Artificial neural networks
Catalysis
Density functional theory
Extrapolation
Machine learning
Metal surfaces
Parameter estimation
Prediction models
Reaction intermediates
Surface chemistry
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Summary:Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the cost of calculating the adsorption energies by DFT for a large number of reaction intermediates can become prohibitive. Here, we have identified appropriate descriptors and machine learning models that can be used to predict part of these adsorption energies given data on the rest of them. Our investigations also included the case when the species data used to train the predictive model is of different size relative to the species the model tries to predict - an extrapolation in the data space which is typically difficult with regular machine learning models. We have developed a neural network based predictive model that combines an established model with the concepts of a convolutional neural network that, when extrapolating, achieves significant improvement over the previous models.
ISSN:2331-8422
DOI:10.48550/arxiv.1910.00623