A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications
Year: 2020
Type: article
Abstract: 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 number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Moreover, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict-this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, University of South Carolina, San Diego Supercomputer Center, National Energy Research Scientific Computing Center, Office of Science, Office of Integrative Activities, Division of Chemical, Bioengineering, Environmental, and Transport Systems, Basic Energy Sciences, Pacific Northwest National Laboratory
Awards: G122673668, G1286236842, G1286251239, G1677143136, G2618837358, DMREF: Collaborative Research: Design and Discovery of Multimetallic Heterogeneous Catalysts for a Future Biorefining Industry, TAS::89 0222::TAS; NEW; THEORETICAL INVESTIGATION OF HETEROGENEOUS CATALYSIS AT THE SOLID-LIQUID INTERFACE FOR THE CONVERSION OF LIGNOCELLULOSIC BI, G30714459, G42916606, G4883577764, G5076365615, G5193797517, G519640106, G5484204987, G5739080780, RII Track-2 FEC: Center for a Sustainable Water, Energy, and Food Nexus (SusWEF), G613759197, G6269097807, G650090426, G6568068330, G8848424823
OpenAlex ID: W2977976293