activity
20122023
most citedPhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges

1.1k citations · 2.2k across the 58 of their papers we have counts for

collaborators
Showing 2019Show all

9 papers · 1 filter

physics.chem-ph2019★ 16 cited

Formation and Stabilization of Ground and Excited State Singlet O upon Recombination of P Oxygen on Amorphous Solid Water

Marco Pezzella, Debasish Koner, Markus Meuwly

The recombination dynamics of P oxygen atoms on cold amorphous solid water to form triplet and singlet molecular oxygen (O) is followed under conditions representative for…

physics.chem-ph2019★ 64 cited

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…

physics.chem-ph2019★ 43 cited

The Reaction: Thermal and Vibrational Relaxation Rates for the A, A and A States

Juan Carlos San Vicente Veliz, Debasish Koner, Max Schwilk +2

The kinetics and vibrational relaxation of the reaction is investigated over a wide temperature ra…

physics.chem-ph2019★ 56 cited

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…

physics.chem-ph2019★ 39 cited

Near dissociation states for H-He on MRCI and FCI potential energy surfaces

Debasish Koner, Juan Carlos San Vicente Veliz, Ad van der Avoird +1

A new analytical potential energy surface (PES) has been constructed for H-He using a reproducing kernel Hilbert space (RKHS) representation from an extensive number of $ab i…

physics.chem-ph2019★ 3 cited

Machine Learning Potential Energy Surfaces

Oliver T. Unke, Markus Meuwly

Machine Learning techniques can be used to represent high-dimensional potential energy surfaces for reactive chemical systems. Two such methods are based on a reproducing kernel Hi…

physics.chem-ph2019★ 68 cited

Long-range vs. short-range effects in cold molecular ion-neutral collisions: Charge exchange of Rb with N and O

Alexander D. Dörfler, Pascal Eberle, Debasish Koner +3

We report a study of cold charge-transfer (CT) collisions of Rb atoms with N and O ions in the mK regime using a dynamic ion-neutral hybrid trapping experiment. State-…

physics.chem-ph2019★ 38 cited

Reactive Atomistic Simulations of Diels-Alder Reactions: the Importance of Molecular Rotations

Uxía Rivero, Oliver T. Unke, Markus Meuwly +1

The Diels-Alder reaction between 2,3-dibromo-1,3-butadiene and maleic anhydride has been studied by means of multisurface adiabatic reactive molecular dynamics and the PhysNet neur…

physics.chem-ph2019★ 1.1k cited

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges

Oliver T. Unke, Markus Meuwly

In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry. After being trained on appropriate ab initio reference data, these metho…