Markus Meuwly

activity

20232026

most citedFeshbach Resonances in Cold Collisions: Benchmarking State of the Art ab initio Potential Energy Surfaces

5 citations · 9 across the 19 of their papers we have counts for

collaborators

20 papers

physics.chem-ph2026

Energy Flow Shapes the Decomposition Dynamics of a Reactive Atmospheric Intermediate

Mitradip Das, Cangtao Yin, M. Meuwly

The unimolecular dynamics of Criegee intermediates depend strongly on their internal energy, which can differ substantially between nascent atmospheric species and laboratory-prepa…

physics.chem-ph2026

Quantitative Classical Relaxation Dynamics of Penning-Ionized He/Ne--H $_{2}^{+}$ /HD $^{+}$ Complexes

Meenu Upadhyay, Baruch Margulis, Octavio Roncero +3

Penning ionization allows {\it in situ} generation of ionic complexes to launch vibrational relaxation and dissociation dynamics for rare gas--H $_{2}^{+}$ /HD $^{+}$ complexes. The ensuing…

physics.chem-ph2026

Charge-partition pathways in strong-field photoionization of carbonyl sulfide monomers and dimers

Chao He, Xinyue Zhang, Cangtao Yin +2

Strong-field photoionization of molecules and molecular clusters gives rise to a rich variety of fragmentation pathways governed by charge localization and redistribution on ultraf…

physics.chem-ph2026

Reaction Pathway Detection using Machine-Learned Energy Potentials -- Decomposition of Energized CF $_{3}$ CHOO

Cangtao Yin, Markus Meuwly

Characterization of the decomposition products of energized Criegee intermediates is essential for assessing their impact on the chemical evolution of the atmosphere. Here, a gener…

physics.chem-ph2026

Full-Dimensional Reactive Potential Energy Surfaces for OCS $^{+}$ $\to$ CO+S $^{+}$ Dissociation: Ground and Excited States

Cangtao Yin, Stefan Willitsch, Markus Meuwly

Full-dimensional reactive potential energy surfaces (PESs) for the OCS $^{+}$ cation are constructed to describe S $^{+}$ loss in the electronic ground state and seven low-lying electron…

physics.chem-ph2026

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…

physics.chem-ph2026

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics

Valerii Andreichev, Jindra Dušek, Markus Meuwly +1

Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning appro…

physics.chem-ph2026

Full Reaction Pathway Dynamics for Atmospheric Decomposition Reactions: The Photodissociation of H $_{2}$ COO

Cangtao Yin, Markus Meuwly

Branching ratios for fragmentation channels of important meta- and unstable species are essential for a molecular-level characterization of atmospheric chemistry. Here, the molecul…

physics.chem-ph2025★ 1 cited

Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions

Sena Aydin, Valerii Andreichev, Pantelis Maragkoudakis +1

Molecular dynamics simulations for tripeptides in the gas phase and in solution using empirical and machine-learned energy functions are presented. For cationic AAA a machine-learn…

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-ph2025

End-to-End Photodissociation Dynamics of Energized H $_{2}$ COO

Cangtao Yin, Silvan Käser, Meenu Upadhyay +1

The end-to-end dynamics of the smallest energized Criegee intermediate, H $_{2}$ COO, was characterized for vibrational excitation close to and a few kcal/mol above the barrier for hyd…

physics.chem-ph2025

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…

physics.chem-ph2025

Augmenting chemical databases for atomistic machine learning by sampling conformational space

Luis Itza Vazquez-Salazar, Markus Meuwly

Machine learning (ML) has become a standard tool for the exploration of chemical space. Much of the performance of such models depends on the chosen database for a given task. Here…

physics.chem-ph2025

Reaction Dynamics of the H + HeH $^{+}$ $\to$ He + H $_{2}^{+}$ System

Meenu Upadhyay, Silvan Käser, Jayakrushna Sahoo +2

The reaction dynamics for the H + HeH $^{+}$ $\to$ He + H $_{2}^{+}$ reaction in its electronic ground state is investigated using two different representations of the potential en…

physics.chem-ph2024★ 2 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★ 5 cited

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…

physics.chem-ph2024★ 1 cited

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…

physics.chem-ph2024

$Asparagus$ : 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…

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…

Energy Flow Shapes the Decomposition Dynamics of a Reactive Atmospheric Intermediate

Quantitative Classical Relaxation Dynamics of Penning-Ionized He/Ne--H2+​/HD+ Complexes

Charge-partition pathways in strong-field photoionization of carbonyl sulfide monomers and dimers

Reaction Pathway Detection using Machine-Learned Energy Potentials -- Decomposition of Energized CF3​CHOO

Full-Dimensional Reactive Potential Energy Surfaces for OCS+ → CO+S+ Dissociation: Ground and Excited States

Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics

Full Reaction Pathway Dynamics for Atmospheric Decomposition Reactions: The Photodissociation of H2​COO

Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions

Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings

End-to-End Photodissociation Dynamics of Energized H2​COO

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions

Augmenting chemical databases for atomistic machine learning by sampling conformational space

Reaction Dynamics of the H + HeH+ → He + H2+​ System

The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks

Feshbach Resonances in Cold Collisions: Benchmarking State of the Art ab initio Potential Energy Surfaces

Accurate Tunneling Splittings for Ever-Larger Molecules from Transfer-Learned, CCSD(T) Quality Energy Functions

Asparagus: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces

Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces

Numerical Accuracy Matters: Applications of Machine Learned Potential Energy Surfaces

Quantitative Classical Relaxation Dynamics of Penning-Ionized He/Ne--H $_{2}^{+}$ /HD $^{+}$ Complexes

Reaction Pathway Detection using Machine-Learned Energy Potentials -- Decomposition of Energized CF $_{3}$ CHOO

Full-Dimensional Reactive Potential Energy Surfaces for OCS $^{+}$ $\to$ CO+S $^{+}$ Dissociation: Ground and Excited States

Full Reaction Pathway Dynamics for Atmospheric Decomposition Reactions: The Photodissociation of H $_{2}$ COO

End-to-End Photodissociation Dynamics of Energized H $_{2}$ COO

Reaction Dynamics of the H + HeH $^{+}$ $\to$ He + H $_{2}^{+}$ System

$Asparagus$ : A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces