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Scalable machine learning model for energy decomposition analysis in aqueous systems.

Hossein Tahmasbi1,2, Michael Beerbaum1,2, Bartosz Brzoza1,2

  • 1Center for Advanced Systems Understanding, 02826 Görlitz, Germany.

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|December 4, 2025
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Summary
This summary is machine-generated.

We developed a neural network model for energy decomposition analysis (EDA) to predict electron delocalization energy. This method accurately models large molecular systems, like metal-organic frameworks, beyond traditional computational limits.

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Area of Science:

  • Computational chemistry
  • Quantum chemistry
  • Materials science

Background:

  • Energy Decomposition Analysis (EDA) using absolutely localized molecular orbitals (ALMOs) is crucial for understanding intermolecular bonding.
  • Accurate calculation of binding energy components is essential for predicting molecular interactions.

Purpose of the Study:

  • To develop a neural network-based EDA model for predicting electron delocalization energy.
  • To enable accurate prediction of electron delocalization energies for large molecular systems.

Main Methods:

  • Development of a neural network model for EDA.
  • Prediction of electron delocalization energy component, focusing on charge transfer stabilization.
  • Utilizing the locality assumption of electronic structure.

Main Results:

  • The neural network EDA model accurately predicts electron delocalization energies.
  • The model's accuracy is maintained for systems significantly larger than those accessible by conventional density functional theory (DFT).
  • Demonstrated applicability in modeling hydration effects in metal-organic frameworks (MOFs).

Conclusions:

  • Neural network EDA offers a powerful approach for studying intermolecular interactions in large systems.
  • This method extends the scale of accurate electronic structure calculations for complex molecular assemblies.
  • The model provides valuable insights into hydration phenomena within materials like MOFs.