Automated Bonding Analysis with Crystal Orbital Hamilton Populations

GND
1145071368
ORCID
0000-0001-8907-0336
Affiliation
Friedrich Schiller University Jena Institute of Condensed Matter Theory and Solid-State Optics Max-Wien-Platz 1 07743 Jena Germany
George, Janine;
Affiliation
Université Catholique de Louvain Institute of Condensed Matter and Nanosciences Chemin des Étoiles 8 1348 Louvain-la-Neuve Belgium
Petretto, Guido;
ORCID
0000-0002-6071-6786
Affiliation
Friedrich Schiller University Jena Institute of Condensed Matter Theory and Solid-State Optics Max-Wien-Platz 1 07743 Jena Germany
Naik, Aakash;
Affiliation
former address: Department of Chemistry University of Oregon Eugene OR 97403 USA
Esters, Marco;
Affiliation
Scientific Computing Department Science & Technology Facilities Council Rutherford Appleton Laboratory Didcot 0X11 0QX UK
Jackson, Adam J.;
Affiliation
RWTH Aachen University Institute of Inorganic Chemistry 52056 Aachen Germany
Nelson, Ryky;
Affiliation
RWTH Aachen University Institute of Inorganic Chemistry 52056 Aachen Germany
Dronskowski, Richard;
Affiliation
Université Catholique de Louvain Institute of Condensed Matter and Nanosciences Chemin des Étoiles 8 1348 Louvain-la-Neuve Belgium
Rignanese, Gian‐Marco;
Affiliation
Université Catholique de Louvain Institute of Condensed Matter and Nanosciences Chemin des Étoiles 8 1348 Louvain-la-Neuve Belgium
Hautier, Geoffroy

Understanding crystalline structures based on their chemical bonding is growing in importance. In this context, chemical bonding can be studied with the Crystal Orbital Hamilton Population (COHP), allowing for quantifying interatomic bond strength. Here we present a new set of tools to automate the calculation of COHP and analyze the results. We use the program packages VASP and LOBSTER , and the Python packages atomate and pymatgen . The analysis produced by our tools includes plots, a textual description, and key data in a machine‐readable format. To illustrate those capabilities, we have selected simple test compounds (NaCl, GaN), the oxynitrides BaTaO 2 N, CaTaO 2 N, and SrTaO 2 N, and the thermoelectric material Yb 14 Mn 1 Sb 11 . We show correlations between bond strengths and stabilities in the oxynitrides and the influence of the Mn−Sb bonds on the magnetism in Yb 14 Mn 1 Sb 11 . Our contribution enables high‐throughput bonding analysis and will facilitate the use of bonding information for machine learning studies.

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