[HTML][HTML] Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

A Omranpour, P Montero De Hijes, J Behler… - The Journal of …, 2024 - pubs.aip.org
As the most important solvent, water has been at the center of interest since the advent of
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …

[HTML][HTML] Asparagus: A toolkit for autonomous, user-guided construction of machine-learned potential energy surfaces

K Töpfer, LI Vazquez-Salazar, M Meuwly - Computer Physics …, 2025 - Elsevier
With the establishment of machine learning (ML) techniques in the scientific community, the
construction of ML potential energy surfaces (ML-PES) has become a standard process in …

[HTML][HTML] PhysNet meets CHARMM: A framework for routine machine learning/molecular mechanics simulations

K Song, S Käser, K Töpfer… - The Journal of …, 2023 - pubs.aip.org
Full-dimensional potential energy surfaces (PESs) based on machine learning (ML)
techniques provide a means for accurate and efficient molecular simulations in the gas and …

[HTML][HTML] Evaluating aliphatic CF, CF2, and CF3 groups as vibrational Stark effect reporters

R Cruz, K Ataka, J Heberle, J Kozuch - The Journal of Chemical …, 2024 - pubs.aip.org
Given the extensive use of fluorination in molecular design, it is imperative to understand the
solvation properties of fluorinated compounds and the impact of the C–F bond on …