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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…
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…
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…
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…