Login / Signup

Automated Bonding Analysis with Crystal Orbital Hamilton Populations.

Janine GeorgeGuido PetrettoAakash Ashok NaikMarco EstersAdam J JacksonRyky NelsonRichard DronskowskiGian-Marco RignaneseGeoffroy Hautier
Published in: ChemPlusChem (2022)
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.
Keyphrases
  • machine learning
  • high throughput
  • deep learning
  • healthcare
  • artificial intelligence
  • single cell
  • big data
  • electronic health record
  • social media