Energy decomposition analysis method with the DFT-in-xTB embedding strategy for intermolecular interactions in large systems

In this work, an energy decomposition analysis (EDA) method, termed DM-EDA(EB), is introduced to explore intermolecular interactions in large systems by employing a DFT-in-xTB embedding scheme. DM-EDA(EB) integrates density matrix-based EDA (DM-EDA) with the GFNn-xTB method to decompose the total interaction energy into electrostatic, exchange-repulsion, polarization, and correlation terms. Test cases demonstrate that DM-EDA(EB) can accurately analyze total interaction energies in large systems with the computational efficiency comparable to GFNn-xTB. Notably, by using the appropriate partition strategy, DM-EDA(EB) is able to provide quantificational knowledge of individual interactions in large assemblies.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2025-03-24
DOI
https://doi.org/10.1063/5.0258177
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Energy decomposition analysis method with the DFT-in-xTB embedding strategy for intermolecular interactions in large systems

Peifeng Su, Wei Wu, Yueyang Zhang, Xuewei Xiong
The Journal of Chemical Physics
Machine Learning in Materials Science
article

Energy decomposition analysis method with the DFT-in-xTB embedding strategy for intermolecular interactions in large systems

Peifeng Su, Wei Wu, Yueyang Zhang, Xuewei Xiong
article en

Abstract

In this work, an energy decomposition analysis (EDA) method, termed DM-EDA(EB), is introduced to explore intermolecular interactions in large systems by employing a DFT-in-xTB embedding scheme. DM-EDA(EB) integrates density matrix-based EDA (DM-EDA) with the GFNn-xTB method to decompose the total interaction energy into electrostatic, exchange-repulsion, polarization, and correlation terms. Test cases demonstrate that DM-EDA(EB) can accurately analyze total interaction energies in large systems with the computational efficiency comparable to GFNn-xTB. Notably, by using the appropriate partition strategy, DM-EDA(EB) is able to provide quantificational knowledge of individual interactions in large assemblies.

The Journal of Chemical PhysicsVol. 162(12)
Xiamen University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 96%
Machine Learning in Materials Science
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