Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

21 May 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

Keywords

machine-learning
high-throughput screening
density functional theory calculations
heterogeneous catalysts
convolutional neural network

Supplementary materials

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