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20192025
most citedThe Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks

2 citations · 2 across the 4 of their papers we have counts for

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Showing physics.chem-phShow all

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physics.chem-ph2025

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…

physics.chem-ph20242 cited

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…

physics.chem-ph2024

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…

physics.chem-ph2023

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…

physics.chem-ph2023

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…

physics.chem-ph2020

ML Models of Vibrating HCO: Comparing Reproducing Kernels, FCHL and PhysNet

Silvan Käser, Debasish Koner, Anders S. Christensen +2

Machine Learning (ML) has become a promising tool for improving the quality of atomistic simulations. Using formaldehyde as a benchmark system for intramolecular interactions, a co…