Dresden 2026 – scientific programme

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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 34: Focus Session: Water – from Atmosphere to Space V (joint session CPP/DY)

CPP 34.1: Talk

Wednesday, March 11, 2026, 11:00–11:15, ZEU/0260

Scalable Machine Learning Model for Energy Decomposition Analysis in Aqueous Systems — •Thomas Kühne — CASUS/HZDR, Görlitz, Germany

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.

Keywords: Liquid Water; Energy decomposition analysis; Machine Learning; Neural Networks