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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 and posted on 21.05.2019 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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