activity
20162026
most citedDeveloping machine-learned potentials to simultaneously capture the dynamics of excess protons and hydroxide ions in classical and path integral simulations

21 citations · 67 across the 9 of their papers we have counts for

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17 papers · 1 filter

physics.chem-ph2026

How Alkali Metal Cations Affect the Structure and Reactivity of the Hydrated Dielectron

Tatiana Nemirovich, Pavel Jungwirth, Ondrej Marsalek

Hydrated electrons of opposite spins pair to form dielectrons at sufficiently high concentrations that can be achieved by dissolution of alkali metals in water. While experimental…

physics.chem-ph2026

More converged, less accurate? Reassessing standard choices for ab initio water using machine learning potentials

Hubert Beck, Ondrej Marsalek

Accurately simulating the properties of liquid water remains a central challenge in molecular simulations. In this work, we use machine learning potentials to investigate how the c…

physics.chem-ph2025★ 5 cited

Multi-head committees enable direct uncertainty prediction for atomistic foundation models

Hubert Beck, Pavol Simko, Lars L. Schaaf +2

Machine learning potentials have become a standard tool for atomistic materials modelling. While models continue to become more generalisable, an open challenge relates to efficien…

physics.chem-ph2024

Elucidating the Nature of -hydrogen Bonding in Liquid Water and Ammonia

Krystof Brezina, Hubert Beck, Ondrej Marsalek

Aromatic compounds form an unusual kind of hydrogen bond with water and ammonia molecules, known as the -hydrogen bond. In this work, we report ab initio path integral molecular…

physics.chem-ph2023★ 21 cited

Developing machine-learned potentials to simultaneously capture the dynamics of excess protons and hydroxide ions in classical and path integral simulations

Austin O. Atsango, Tobias Morawietz, Ondrej Marsalek +1

The transport of excess protons and hydroxide ions in water underlies numerous important chemical and biological processes. Accurately simulating the associated transport mechanism…

physics.chem-ph2023★ 18 cited

Reducing the cost of neural network potential generation for reactive molecular systems

Krystof Brezina, Hubert Beck, Ondrej Marsalek

Although machine-learning potentials have recently had substantial impact on molecular simulations, the construction of a robust training set can still become a limiting factor, es…