5 papers
ML Models of Vibrating HCO: Comparing Reproducing Kernels, FCHL and PhysNet
Silvan Käser, Debasish Koner, Anders S. Christensen +2
Machine Learning (ML) has become a promising tool for improving the quality of atomistic simulations. Using formaldehyde as a benchmark system for intramolecular interactions, a co…
Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions
Julian Arnold, Debasish Koner, Silvan Käser +3
Machine learning-based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested.…
Isomerization and Decomposition Reactions of Acetaldehyde Relevant to Atmospheric Processes from Dynamics Simulations on Neural Network-Based Potential Energy Surfaces
Silvan Käser, Oliver T. Unke, Markus Meuwly
Acetaldehyde (AA) isomerization (to vinylalcohol, VA) and decomposition (into either CO+CH and H+HCCO) is studied using a fully dimensional, reactive potential energy s…
Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces
Silvan Käser, Oliver T. Unke, Markus Meuwly
The in silico exploration of chemical, physical and biological systems requires accurate and efficient energy functions to follow their nuclear dynamics at a molecular and atomisti…
High-Dimensional Potential Energy Surfaces for Molecular Simulations
Oliver T. Unke, Debasish Koner, Sarbani Patra +2
An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, com…