Scalable Machine Learning Model for Energy Decomposition Analysis in Aqueous Systems
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914053910167552 |
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| author | Tahmasbi, Hossein Beerbaum, Michael Brzoza, Bartosz Cangi, Attila Kühne, Thomas D. |
| author_facet | Tahmasbi, Hossein Beerbaum, Michael Brzoza, Bartosz Cangi, Attila Kühne, Thomas D. |
| contents | Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular binding energy into physically meaningful components. Here, we develop a neural network EDA model capable of predicting the electron delocalization energy component of water molecules, which captures the stabilization arising from charge transfer between occupied absolutely localized molecular orbitals of one molecule and the virtual orbitals of another. Exploiting the locality assumption of the electronic structure, our model enables accurate prediction of electron delocalization energies for molecular systems far beyond the size accessible to conventional density functional theory calculations, while maintaining its accuracy. We demonstrate the applicability of our approach by modeling hydration effects in large molecular complexes, specifically in metal-organic frameworks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_19983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Machine Learning Model for Energy Decomposition Analysis in Aqueous Systems Tahmasbi, Hossein Beerbaum, Michael Brzoza, Bartosz Cangi, Attila Kühne, Thomas D. Chemical Physics Materials Science Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular binding energy into physically meaningful components. Here, we develop a neural network EDA model capable of predicting the electron delocalization energy component of water molecules, which captures the stabilization arising from charge transfer between occupied absolutely localized molecular orbitals of one molecule and the virtual orbitals of another. Exploiting the locality assumption of the electronic structure, our model enables accurate prediction of electron delocalization energies for molecular systems far beyond the size accessible to conventional density functional theory calculations, while maintaining its accuracy. We demonstrate the applicability of our approach by modeling hydration effects in large molecular complexes, specifically in metal-organic frameworks. |
| title | Scalable Machine Learning Model for Energy Decomposition Analysis in Aqueous Systems |
| topic | Chemical Physics Materials Science |
| url | https://arxiv.org/abs/2509.19983 |