<scp>LOPOSTER</scp>: A Cascading Postprocessor for <scp>LOBSTER</scp>

The computer program LOPOSTER, available via GitHub, is introduced, capable of postprocessing the LOBSTER code results. LOPOSTER is designed to be particularly effective for analyzing large datasets with over 10,000 interactions and enormously reducing postprocessing time. LOPOSTER pioneers the automated processing of advanced bonding analysis results, including multicenter bonding, molecular-orbital formation energy, and k-dependent COHP, expanding the scope of routine chemical-bonding investigations. In addition, LOPOSTER streamlines the postprocessing workflow by providing comprehensive results in a single execution, minimizing user intervention and potential errors. An example of chemical-bonding analysis on NiNCN is provided, with visualization by LOPOSTER. LOPOSTER offers versatile analysis of interactions in NiNCN, enabling evaluations in real or reciprocal space, and based on atomic or molecular orbitals, catering to different analytic preferences. Various correlations between those interactions and magnetism in NiNCN are also explored. The electron-rich features of an N=C=N π bond have been discussed from various perspectives.

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Publication Details

Journal
Journal of Computational Chemistry
Published
2025-06-30
DOI
https://doi.org/10.1002/jcc.70167
Citations
7
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.62
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article

LOPOSTER: A Cascading Postprocessor for LOBSTER

Yixu Wang, Richard Dronskowski, Peter C. Müller, David Schnieders
7 citations
Journal of Computational Chemistry
Machine Learning in Materials Science
3.62
article

LOPOSTER: A Cascading Postprocessor for LOBSTER

Yixu Wang, Richard Dronskowski, Peter C. Müller, David Schnieders
article en
7 citations

Abstract

The computer program LOPOSTER, available via GitHub, is introduced, capable of postprocessing the LOBSTER code results. LOPOSTER is designed to be particularly effective for analyzing large datasets with over 10,000 interactions and enormously reducing postprocessing time. LOPOSTER pioneers the automated processing of advanced bonding analysis results, including multicenter bonding, molecular-orbital formation energy, and k-dependent COHP, expanding the scope of routine chemical-bonding investigations. In addition, LOPOSTER streamlines the postprocessing workflow by providing comprehensive results in a single execution, minimizing user intervention and potential errors. An example of chemical-bonding analysis on NiNCN is provided, with visualization by LOPOSTER. LOPOSTER offers versatile analysis of interactions in NiNCN, enabling evaluations in real or reciprocal space, and based on atomic or molecular orbitals, catering to different analytic preferences. Various correlations between those interactions and magnetism in NiNCN are also explored. The electron-rich features of an N=C=N π bond have been discussed from various perspectives.

Journal of Computational ChemistryVol. 46(17)
RWTH Aachen University (DE)
Openalex Percentile: Top 7%
Machine Learning in Materials Science
3.62
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