134 citations · 437 across the 17 of their papers we have counts for
18 papers
Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings
Valerii Andreichev, Silvan Käser, Erica L. Bocanegra +3
The infrared spectroscopy and proton transfer dynamics together with the associated tunneling splittings for H/D-transfer in oxalate are investigated using a machine learning-based…
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
Silvan Käser, Debasish Koner, Markus Meuwly
Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins, and materials on wide time and length scales. Their reliability and predictiveness, howe…
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
Luis Itza Vazquez-Salazar, Silvan Käser, Markus Meuwly
Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods - Ensembles…
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…