5 papers · 1 filter
Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate
Chen Qu, Paul L. Houston, Qi Yu +5
There has been a veritable explosion of methods and software to perform machine-learned regression on datasets of electronic energies and forces to develop high-dimensional machine…
End-to-End Photodissociation Dynamics of Energized HCOO
Cangtao Yin, Silvan Käser, Meenu Upadhyay +1
The end-to-end dynamics of the smallest energized Criegee intermediate, HCOO, was characterized for vibrational excitation close to and a few kcal/mol above the barrier for hyd…
Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions
Eric D. Boittier, Silvan Käser, Markus Meuwly
Accurate, yet computationally efficient energy functions are essential for state-of-the art molecular dynamics (MD) studies of condensed phase systems. Here, a generic workflow bas…
Reaction Dynamics of the H + HeH He + H System
Meenu Upadhyay, Silvan Käser, Jayakrushna Sahoo +2
The reaction dynamics for the H + HeH He + H reaction in its electronic ground state is investigated using two different representations of the potential en…
Accurate Tunneling Splittings for Ever-Larger Molecules from Transfer-Learned, CCSD(T) Quality Energy Functions
Silvan Käser, Jeremy O. Richardson, Markus Meuwly
This work combines state-of-the-art machine learning techniques with highest-level electronic structure calculations and full-dimensional quantum tunneling calculations to obtain a…