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T. Kühne

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Preprint Sep 2026

On-Water Surface Catalysis: From Hydrogen Bonding to Charge-Transfer Activation

On-water catalysis accelerates reactions between poorly soluble organic substrates in aqueous suspensions, but its molecular origin remains debated. This Account argues that hydrogen bonding and proton transfer can both enhance charge-transfer stabilization between the organic reactants. Hydrogen bonds from surface wat...

M. A. Salem, T. Kühne · 0 citations
Preprint Sep 2026

Native implementation of the machine-learned Skala exchange-correlation functional in CP2K: Unified one-centre reconstruction for molecular and condensed-phase calculations

We implement the machine-learned Skala exchange-correlation (XC) functional natively in CP2K using its Gaussian and plane-wave (GPW) and Gaussian and augmented-plane-wave (GAPW) methods. A joint one-centre reconstruction of density, density gradients, and kinetic-energy density before functional evaluation preserves mi...

Johann Pototschnig, Franz Pöschel, Jürg Hutter et al. · 0 citations
Open access Sep 2026

Periodic GFN2- x TB in CP2K: Multipolar Ewald Electrostatics, k-Point Sampling, and Transferability Benchmarks for Solids

Extended tight-binding methods are attractive for condensed-phase simulations because they retain an explicit electronic Hamiltonian at a cost far below conventional density functional theory. Their transferability to periodic materials, however, cannot be assessed reliably without a genuinely periodic Hamiltonian, B...

Vahideh Alizadeh, Johann V. Pototschnig, L. M. Seidler et al. · 0 citations
Preprint Aug 2026

Molecular Implementation of the Machine-Learned Skala Exchange-Correlation Functional in CP2K through GauXC

Machine-learned exchange--correlation (XC) functionals offer a route to improve Kohn--Sham density-functional theory without incurring the cost of explicitly correlated electronic-structure methods. Their use in production simulation codes, however, requires a well-defined mapping between the learned model and the host...

Franz Pöschel, Johann Pototschnig, Frederick Stein et al. · 0 citations
Open access Jul 2026

Curvature-assisted minima hopping on Lennard–Jones energy landscapes: reusing quasi-Newton information for escape directions

Global structure optimization in computational chemistry is often limited not by leaving the current local minimum, which can be achieved by sufficiently large random moves, but by proposing productive moves that exploit local funnel structure without losing diversity. Minima hopping addresses this problem through shor...

Daniel Schärf, T. Kühne · 0 citations
Open access Sep 2026

Seamless QM/MM Simulations via a GROMACS-CP2K Interface

This work presents a robust and fully periodic QM/MM interface between the open-source molecular dynamics engine GROMACS and the electronic structure theory code CP2K that enables efficient and reproducible QM/MM molecular dynamics and enhanced sampling simulations with a consistent treatment of long-range electrostati...

D. Morozov, C. Blau, Ole Schütt et al. · 0 citations
Open access Jul 2026

Hydrogen-Bond Scalar Couplings as Covalency-Sensitive NMR Fingerprints of Amorphous Ice

Through-hydrogen-bond scalar couplings are attractive NMR observables because they connect high-precision spectroscopy with local hydrogen-bond structure. It is less clear whether they can also report hydrogen-bond covalency in amorphous ice and other frozen or heterogeneous aqueous environments. Here, we combine ab in...

Hossam Elgabarty, T. Kühne · 0 citations
Review Jul 2026

CP2K: An electronic structure and molecular dynamics software package - Dynamics, Transport, and Spectroscopic Response

The present work revisits the methods within CP2K that turn electronic structure into dynamics, transport, and spectroscopic response, highlighting CP2K's unique capability to unify quantum chemistry with quantum and statistical mechanics within a versatile, holistic simulation environment.

Jan Wilhelm, Anna-Sophia Hehn, Hossam Elgabarty et al. · 1 citation · ⚡1
Preprint Jul 2026

MANDALA: An E(3)-Equivariant Graph Neural Network Framework for Learning Electronic-Structure Operators with Observable Guidance

Mandala is a modular software framework for learning block-sparse electronic-structure matrices with E(3)-equivariant graph neural networks that connects electronic-structure learning and observable-guided modeling while retaining a representation tied to quantum-mechanical operators rather than only scalar or vector t...

B. Brzoza, Wiktoria Szopa, Z. Elabid et al. · 0 citations

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