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Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures

preprint
submitted on 18.06.2020 and posted on 22.06.2020 by Hillary Pan, Alex Ganose, Matthew Horton, Muratahan Aykol, Kristin Persson, Nils Zimmermann, Anubhav Jain
Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally-derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against 7 existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to be the most accurate overall. For each algorithm, we also assess computational demand and sensitivity towards small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms and improve the accuracy of structural descriptors for machine learning and other applications.

Funding

U.S. Department of Energy Basic Energy Sciences (BES) –The Materials Project – program Grant No. KC23MP

U.S. Department of Energy Award No. DE-AC02-05CH11231

History

Email Address of Submitting Author

ajain@lbl.gov

Institution

Lawrence Berkeley National Laboratory

Country

United States of America

ORCID For Submitting Author

0000-0001-5893-9967

Declaration of Conflict of Interest

No conflict of interest

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