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Predicting Intermetallic Surface Energies with High-Throughput DFT and Convolutional Neural Networks

preprint
submitted on 05.07.2019 and posted on 08.07.2019 by Aini Palizhati, Wen Zhong, Kevin Tran, Zachary Ulissi
Surface energy of inorganic crystals is crucial in understanding experimentally-relevant surface properties and thus important in designing materials for many applications including catalysis. Predictive methods and datasets exist for surface energies of monometallic crystals but predicting these properties for bimetallic or more complicated surfaces is an open challenge. Here we present a workflow for predicting surface energies \textit{ab initio} using high-throughput DFT and a machine learning framework. We calculate the surface energy of 3,285 intermetallic alloys with combinations of 36 elements and 47 space groups. We used this high-throughput workflow to seed a database of surface energies, which we used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model was able to predict surface energies with a mean absolute test error of 0.0082 eV/angstrom^2 and can qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into which surfaces are more stable and therefore more realistic. It allows us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalysts screening and nanomaterials synthesis.

History

Email Address of Submitting Author

zulissi@andrew.cmu.edu

Institution

Carnegie Mellon University

Country

United States

ORCID For Submitting Author

0000-0002-9401-4918

Declaration of Conflict of Interest

no conflict of interest

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