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

preprint
submitted on 19.05.2019, 18:04 and posted on 21.05.2019, 15:18 by Seoin Back, Junwoong Yoon, Nianhan Tian, Wen Zhong, Kevin Tran, Zachary Ulissi
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.

History

Email Address of Submitting Author

seoinb@andrew.cmu.edu

Institution

Carnegie Mellon University

Country

USA

ORCID For Submitting Author

0000-0003-4682-0621

Declaration of Conflict of Interest

N/A

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