papers

Publications (26)

cond-mat.mtrl-sci2002

Density-functional study of the structure and stability of ZnO surfaces

B. Meyer, Dominik Marx

An extensive theoretical investigation of the nonpolar (100) and (110) surfaces as well as the polar zinc terminated (0001)--Zn and oxygen terminated (000$\bar{1}…

physics.chem-ph2025

Resolving the Nature of the Lowest-Frequency Raman Mode of Liquid Water

Florian Pabst, Harald Forbert, Dominik Marx

The lowest-frequency Raman mode of water, observed through depolarized light scattering or optical Kerr effect techniques, is routinely used to track dynamic changes in water molec…

physics.chem-ph2023

Manifestations of Local Supersolidity of He around a Charged Molecular Impurity

Fabien Brieuc, Christoph Schran, Dominik Marx

A frozen, solid helium core, dubbed snowball, is typically observed around cations in liquid helium. Here we discover, using path integral simulations, that around a cationic molec…

physics.chem-ph2024

Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water

Nore Stolte, János Daru, Harald Forbert +2

Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of the system of interest. As the construction o…

physics.chem-ph2024

When Theory Meets Experiment: What Does it Take to Accurately Predict H NMR Dipolar Relaxation Rates in Neat Liquid Water from Theory?

Dietmar Paschek, Johanna Busch, Angel Mary Chiramel Tony +5

In this contribution, we compute the H nuclear magnetic resonance (NMR) relaxation rate of liquid water at ambient conditions. We are using structural and dynamical information…

physics.chem-ph2026

Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity

Nore Stolte, Harald Forbert, Yury Lysogorskiy +2

The paper presents a data‑efficient machine‑learning interatomic potential, trained on CCSD(T) data, that accurately captures the balance of π‑hydrogen bonding and hydrophobic solv…

#machine learning potentials#aromatic solvation#pi‑hydrogen bonding#hydrophobic effects