PyOrbb —Automated Analyses of Orbital‐Interaction Mechanisms

ABSTRACT We present PyOrbb, an open‐source, user‐friendly Python‐based program that automates the analysis and visualization of orbital interaction mechanisms between molecular fragments or reactants, based on quantum‐chemical computations with the Amsterdam Density Functional (ADF) program. PyOrbb extracts the essential mixing patterns, presents them in an interactively adjustable orbital interaction diagram, and summarizes all relevant quantitative data in a convenient Excel spreadsheet for further analysis. The tool enables efficient interpretation of chemical bonding across a broad range of systems, including examples from organic, inorganic, and supramolecular chemistry. The application runs on macOS and Windows operating systems. PyOrbb is shown to accurately and rapidly visualize relevant bonding mechanisms from the often‐overwhelming output of density functional computations.

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Journal
Journal of Computational Chemistry
Published
2026-09-30
DOI
https://doi.org/10.1002/jcc.70512
Primary Topic
Machine Learning in Materials Science
Type
article
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PyOrbb —Automated Analyses of Orbital‐Interaction Mechanisms

Xiaobo Sun, F. Matthias Bickelhaupt, Célia Fonseca Guerra, Jordi Poater et al.
Journal of Computational Chemistry
Machine Learning in Materials Science
article

PyOrbb —Automated Analyses of Orbital‐Interaction Mechanisms

Xiaobo Sun, F. Matthias Bickelhaupt, Célia Fonseca Guerra, Jordi Poater, Steven E. Beutick, Laurens De Groot, Trevor A. Hamlin, Yuman Hordijk, Tori Gijzen
article en

Abstract

ABSTRACT We present PyOrbb, an open‐source, user‐friendly Python‐based program that automates the analysis and visualization of orbital interaction mechanisms between molecular fragments or reactants, based on quantum‐chemical computations with the Amsterdam Density Functional (ADF) program. PyOrbb extracts the essential mixing patterns, presents them in an interactively adjustable orbital interaction diagram, and summarizes all relevant quantitative data in a convenient Excel spreadsheet for further analysis. The tool enables efficient interpretation of chemical bonding across a broad range of systems, including examples from organic, inorganic, and supramolecular chemistry. The application runs on macOS and Windows operating systems. PyOrbb is shown to accurately and rapidly visualize relevant bonding mechanisms from the often‐overwhelming output of density functional computations.

Journal of Computational ChemistryVol. 47(26)
Institució Catalana de Recerca i Estudis Avançats (ES), Radboud University Nijmegen (NL), University of Johannesburg (ZA), Universitat de Barcelona (ES), Vrije Universiteit Amsterdam (NL)
Openalex Percentile: Top 26%
Machine Learning in Materials Science
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PyOrbb —Automated Analyses of Orbital‐Interaction Mechanisms — Xiaobo Sun, F. Matthias Bickelhaupt, et al. · Journal of Computational Chemistry (2026) | TGRS Research Map | TGRS