134 citations · 437 across the 17 of their papers we have counts for
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Numerical Accuracy Matters: Applications of Machine Learned Potential Energy Surfaces
Silvan Käser, Markus Meuwly
The role of numerical accuracy in training and evaluating neural network-based potential energy surfaces is examined for different experimental observables. For observables that re…
On the Effect of Aleatoric and Epistemic Errors on the Learnability and Quality of NN-based Potential Energy Surfaces
S. Goswami, S. Käser, R. J. Bemish +1
The effect of noise in the input data for learning potential energy surfaces (PESs) based on neural networks for chemical applications is assessed. Noise in energies and forces can…
PhysNet Meets CHARMM: A Framework for Routine Machine Learning / Molecular Mechanics Simulations
Kaisheng Song, Silvan Käser, Kai Töpfer +2
Full dimensional potential energy surfaces (PESs) based on machine learning (ML) techniques provide means for accurate and efficient molecular simulations in the gas- and condensed…
Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions
Ardita Kilaj, Silvan Käser, Jia Wang +7
Recent advances in experimental methodology enabled studies of the quantum-state and conformational dependence of chemical reactions under precisely controlled conditions in the ga…
Transfer-Learned Potential Energy Surfaces: Towards Microsecond-Scale Molecular Dynamics Simulations in the Gas Phase at CCSD(T) Quality
Silvan Käser, Markus Meuwly
The rise of machine learning has greatly influenced the field of computational chemistry, and that of atomistic molecular dynamics simulations in particular. One of its most exciti…