5 citations · 9 across the 19 of their papers we have counts for
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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…
Feshbach Resonances in Cold Collisions: Benchmarking State of the Art ab initio Potential Energy Surfaces
Karl P. Horn, Meenu Upadhyay, Baruch Margulis +4
High-quality potential energy surfaces (PES) are a prerequisite for quantitative atomistic simulations, with both quantum and classical dynamics approaches. The ultimate test for t…
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…
: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces
Kai Töpfer, Luis Itza Vazquez-Salazar, Markus Meuwly
With the establishment of machine learning (ML) techniques in the scientific community, the construction of ML potential energy surfaces (ML-PES) has become a standard process in p…
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…