arXiv subjects

Markus Meuwly

Publications and source records attributed to Markus Meuwly.

At least 19 recordsLinked to original sources

arXiv2026

Site-Specific Vibrational Dynamics to Probe Local and Global Protein Motions

Vibrational spectroscopic probes can provide site-specific information on protein structure and dynamics. In this work, the possibility to relate protein motion with the vibrational response for --SCN, --N$_3$, and --SNO labels covalently linked to all alanine-residues in lysozyme is investigated. Depending on the position of the probe along the polypeptide chain, its local hydration, and the dynamics of its environment, the vibrational response can encode not only local fluctuations but also low-frequency, collective motions of the protein. The spectroscopic probes are described using machine-learning-based models for both bonded interactions, represented by reproducing-kernel models, and electrostatic interactions, represented by fluctuating minimally distributed charges. Spectroscopic dynamics are characterized through frequency fluctuation correlation functions (FFCFs). For many probe locations, the FFCFs contain a non-decaying component on the simulation time scale, indicating residual slow dynamics and incomplete sampling of the underlying conformational fluctuations. The magnitude of these static contributions is consistent with previous experimental observations, providing qualitative validation of the simulations. Overall, the results demonstrate that site-specific vibrational probes can report on dynamics extending beyond their immediate local environment and can, at suitable locations, provide information on collective protein motions.

physics.chem-ph↗

arXiv2026

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

Strong-field photoionization of molecules and molecular clusters gives rise to a rich variety of fragmentation pathways governed by charge localization and redistribution on ultrafast timescales. Here, we report a velocity-map imaging study of the strong-field photoionization and fragmentation of carbonyl sulfide (OCS) monomers and dimers driven by 150 femtosecond (fs) laser pulses at 775~nm. The images of the total kinetic-energy and angular distributions of the OCS$^{2+}$, S$^+$, and CO$^+$ fragments were interpreted with the help of electronic-structure calculations of the potential energy surfaces for OCS$^+$ and OCS$^{2+}$. We identify distinct dissociation pathways of singly and doubly ionized OCS, including two-body breakup channels of OCS$^+$ into $\mathrm{S}^+ + \mathrm{CO}$ and $\mathrm{CO}^+ + \mathrm{S}$, dissociation of OCS$^{2+}$ into $\mathrm{S}^+ + \mathrm{CO}$$^+$ as well as higher-order three-body fragmentation. In addition, the images of the OCS$^{2+}$ channel exhibit near-zero-momentum components, low-energy isotropic features, and highly anisotropic contributions at high kinetic energies that cannot be explained by monomer ionization alone. Analysis of the KER distributions and angular anisotropies indicates that these features originate from the breakup of multiply charged OCS dimers ((OCS)$_2^{2+}$, (OCS)$_2^{3+}$, and (OCS)$_2^{4+}$) through charge-separation channels. Our results illustrate how dynamic signatures of strong-field fragmentation evolve from intramolecular dissociation in isolated molecules to intermolecular charge separation in weakly bound clusters providing a unified picture of charge-driven dissociation dynamics beyond the single-molecule limit.

physics.chem-ph↗

arXiv2026

Reaction Pathway Detection using Machine-Learned Energy Potentials -- Decomposition of Energized CF$_3$CHOO

Characterization of the decomposition products of energized Criegee intermediates is essential for assessing their impact on the chemical evolution of the atmosphere. Here, a generic and microscopically resolved approach is used to determine the molecular fragmentation pathways and products for CF$_3$CHOO. They include, among others, direct formation of CO$_2$ + CHF$_3$ (HFC-23), HF + CO$_2$ + CF$_2$, and fragmentation routes that are not evident from static reaction path calculations alone. The computed probability for formation of HFC-23 of 14 \% qualitatively agrees with a value of $(7.9^{+0.4}_{-0.2})$ \% from recent measurements, given the differences in the two approaches. Non-statistical dynamics is found for almost all decomposition pathways and the simulations show that excess energy can redirect reaction outcomes away from minimum-energy pathways. The results highlight the power of machine-learned PESs to elucidate multi-step reaction mechanisms of atmospherically relevant intermediates beyond traditional Master equation/electronic structure approaches to provide molecular-level understanding of the role of dynamics.

physics.chem-ph↗

arXiv2026

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

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 electronically excited states. High-level \textit{ab initio} reference energies were computed at the MRCI+Q/aug-cc-pVTZ level and were used to generate PESs employing reproducing kernel Hilbert space representations (RKHS). The PESs accurately reproduce the measured dissociation limits to CO(X$^1\Sigma^+$)+S$^+$ in different electronic states. The topology of the PESs reveals multiple linear and T-shaped minima, pronounced angular anisotropy, and state-crossing manifolds. Exploratory quasi-classical trajectory simulations on selected PESs confirm numerical stability and energy conservation, illustrating the suitability of the surfaces for dynamical applications. The present work represents the most comprehensive characterization to date of the lowest PESs of OCS$^+$ and provides a reliable foundation for future studies of the photodissociation of OCS$^+$ and the chem-ionization dynamics of OCS.

physics.chem-ph↗

arXiv2026

Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate

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 learned potential energy surfaces (ML-PESs). A major, but not deeply-studied aspect is how well different ML-PESs represent the same dataset on which they are trained, beyond the standard fitting precision metrics. Here, this is examined in detail using several ''stress tests'', for two widely applied machine-learned potential approaches. One is based on permutationally invariant polynomial (PIP) linear least square regression and the other is the message-passing neural network PhysNet approach. These potentials and dipole moment surfaces are used in VSCF/VCI calculations of vibrational energies and wavefunctions. The energies from the two PESs are directly compared as are the IR spectra. In addition, tunneling splittings for the hydrogen transfer between two equivalent structures are reported from using three methods: ring polymer instanton theory, diffusion Monte Carlo simulations, and the $Q_{im}$ path method. These calculations require the evaluation of on the order of one billion energies that are widely dispersed in the 15-dimensional configurational space. The two PESs yield results for these quantities in excellent agreement with each other.

physics.chem-ph↗

arXiv2026

Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations

A new pairwise hybrid machine-learning/molecular mechanics (ML/MM) potential is introduced that is conceived for application to large, heterogeneous condensed-phase systems. The PhysNet ML method describes monomers and short-range dimer interactions, while a classical MM force field describes pairwise interactions beyond a defined switching distance. Models are fitted to MP2 dimer and pairwise cluster energies, and the quality of each model is assessed at different switching distances and using MM approaches with and without detailed distributed charge electrostatics. The applicability of the approach to molecular dynamics simulations is demonstrated for a basic implementation applied to a small model system. Dichloromethane and acetone are used as test systems to demonstrate the accuracy of the approach in describing pairwise reference data, and also to highlight the limitations of the pairwise approach for systems that exhibit significant many-body effects in condensed phase, paving the way for the addition of a general many-body correction in future work.

physics.chem-ph↗

arXiv2026

Towards Quantitative Reaction Dynamics of O3

The reaction dynamics of O(3P) + O2(3Sigma_g-) collisions in the O3(1A') electronic ground state is characterized on a high-level MRCI+Q/aug-cc-pVQZ potential energy surface represented as a reproducing kernel. For the atom exchange reactions involving the ^{16}O and ^{18}O isotopes as the atomic collision partner, associated with rates k6(T) and k8(T), respectively, a negative temperature-dependence of k(T), consistent with experiments was found. The absolute rates typically underestimate measured rates by 50 percent, depending on the experiment considered. For the ratio R(T) = k8(T)/k6(T), the measured T-dependence was found, including a cusp at lower temperatures. The differences between experiments and computations are primarily due to neglect of quantum effects, primarily zero-point effects. For the atomization reaction, leading to 3O(3P), the rates is lower by approximately one order of magnitude compared with experiments, which is a clear improvement over simulations using previous potential energy surfaces computed with smaller basis sets. Non-adiabatic effects are deemed minor for the atom exchange reactions.

physics.chem-ph↗

arXiv2026

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

Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of this workflow without any loss of accuracy. We discuss various methods for constructing potential energy surfaces including transfer learning, which requires a minimal number of expensive training points. In this way, we can study chemical reactions at a high level but a low cost. In particular, as the potentials are smooth and differentiable, they enable the use of more advanced semiclassical approximations to quantum dynamics, such as perturbatively corrected instanton theory, which can capture both tunnelling and anharmonicity.

physics.chem-ph↗

arXiv2026

Efficient, Equivariant Predictions of Distributed Charge Models

A machine learning (ML) based equivariant neural network for constructing distributed charge models (DCMs) of arbitrary resolution, DCM-net, is presented. DCMs efficiently and accurately model the anisotropy of the molecular electrostatic potential (ESP) and go beyond the point charge representation used in conventional molecular mechanics (MM) energy functions. This is particularly relevant for capturing the conformational dependence of the ESP (internal polarization) and chemically relevant features such as lone pairs or {\sigma}-holes. Across conformational space, the learned charge positions from DCM-net are stable and continuous. Across the QM9 chemical space, two-charge-per-atom models achieve accuracies comparable to fitted atomic dipoles for previously unseen molecules (0.75 (kcal/mol)/e). Three- and four-charge-per-atom models reach accuracies competitive with atomistic multipole expansions up to quadrupole level (0.55 (kcal/mol)/e). Pronounced improvements of the ESP are found around O and F atoms, both of which are known to feature strongly anisotropic fields, and for aromatic systems. Across the QM9 reference data set, molecular dipole moments improve by 0.1 D compared with fitted monopoles. Transfer learning on dipeptides yields a 0.2 (kcal/mol)/e ESP improvement for unseen samples and a two-fold MAE reduction for molecular dipole moments versus fitted monopoles. Overall, DCM-net offers a fast and physically meaningful approach to generating distributed charge models for running pure ML or mixed ML/MM based molecular simulations. level (0.55 (kcal/mol)/e).

physics.chem-ph↗

arXiv2026

Full Reaction Pathway Dynamics for Atmospheric Decomposition Reactions: The Photodissociation of H$_2$COO

Branching ratios for fragmentation channels of important meta- and unstable species are essential for a molecular-level characterization of atmospheric chemistry. Here, the molecular product channels for the decomposition dynamics of the smallest Criegee intermediate, H$_2$COO, are quantitatively investigated. Using a high-quality, full-dimensional machine learned potential energy surface (CASPT2/aug-cc-pVTZ), the translational, rotational, and vibrational energy distributions of the CO$_2$+H$_2$, H$_2$O+CO, and HCO+OH fragmentation channels were analyzed to elucidate partitioning of the available energy. The CO$_2$ + H$_2$ product forms through two different pathways that bifurcate after formation of the OCH$_2$O intermediate. Along the direct pathway, CO$_2$ is preferentially vibrationally excited with H$_2$in its vibrational ground state, whereas for the indirect pathway going through formic acid, H$_2$ can populate levels with $v > 0$. For all product channels passing through energized formic acid, the lifetime distributions are described by stretched exponentials with $\beta$ ranging from 1.1 to 1.7. This is a clear signature of non-RRKM effects and suggests that the explicit molecular dynamics needs to be followed for a quantitative and realistic description of the photodissociation dynamics.

physics.chem-ph↗

arXiv2025

A State-Space-View of Atom-Diatom Reactions Relevant to Rarefied Gas Flow

A microscopically resolved picture of energy flow in atom-diatom collisions is essential for understanding the non-equilibrium chemistry in rarefied and hypersonic gas flow. Here, a comprehensive ensemble of quasi-classical trajectories on global, reactive, and ``vetted'' potential energy surfaces are employed to construct state-resolved probability maps and to determine the dependence of the outcomes on the initial ro-vibrational states $(v,j)$. The full range of processes, including elastic, inelastic, atom exchange, reactive, and atomization are quantified, revealing distinct structure reactivity relationships. For the [OOO] system consistent trends are obtained from two high-quality potential energy surfaces, despite their different electronic structure and representation techniques. The resulting state-space description provides a comprehensive picture of energy redistribution in high-energy atom-diatom collisions, forming a basis for improved modeling of non-equilibrium chemistry in hypersonic and rarefied environments.

physics.chem-ph↗

arXiv2025

Structure and Spectroscopy of Criegee Intermediates in Gas- and Aqueous Environments

The dynamics and spectroscopy of the small (H$_2$COO) and large (CH$_3$CHOO) Criegee intermediates (CIs) in the gas phase, inside/on water droplets, on amorphous solid water (ASW) and in bulk water are investigated using validated energy functions. For both species, facile diffusion between surface and inside positions for water droplets are found whereas on amorphous solid water at low temperatures (50 K) no surface diffusion is observed on the multiple-nanosecond time scale. This is at variance with other species, such as CO or NO on ASW. The infrared spectroscopy of both CIs in contact with an aqueous environment leads to shifts of the spectral features on the order of a few to a few tens of cm$^{-1}$, depending on the vibrational mode considered. This is consistent with Stark-induced spectral shifts for small molecules in protein environments. However, the spectroscopy of both CIs in contact with water droplets does not depend on the positioning relative to the droplet (inside vs. surface).

physics.chem-ph↗

arXiv2025

Design, Assessment, and Application of Machine Learning Potential Energy Surfaces

Potential Energy Surfaces (PESs) are an indispensable tool to investigate, characterise and understand chemical and biological systems in the gas and condensed phases. Advances in Machine Learning (ML) methodologies have led to the development of Machine Learned Potential Energy Surfaces (ML-PES) which are now widely used to simulate such systems. The present work provides an overview of concepts, methodologies and recommendations for constructing and using ML-PESs. The choice of topics is focused on practical and recurrent issues to conceive and use such model. Application of the principles discussed are illustrated through two different systems of biomolecular importance: the non-reactive dynamics of the Alanine-Lysine-Alanine tripeptide in gas and solution phases, and double proton transfer reactions in DNA base pairs.

physics.chem-ph↗

arXiv2025

Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions

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-learned potential energy surface (ML-PES) trained on MP2 reference data yields quantitative agreement with measured splittings of the amide-I vibrations. Experimental spectroscopy in solution reports a splitting of 25 cm-1 which compares with 20 cm-1 from ML/MM-MD simulations of AAA in explicit solvent. For the AMA tripeptide a ML-PES describing both, the zwitterionic and neutral form is trained and used to map out the accessible conformational space. Due to cyclization and H-bonding between the termini in neutral AMA the NH- and OH-stretch spectra are strongly red-shifted below 3000 cm-1. The present work demonstrates that meaningful MD simulations on the nanosecond time scale are feasible and provides insight into experiments.

physics.chem-ph↗

arXiv2025

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations

Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail and accuracy. Here, kernel- and neural network-based approaches for intramolecular degrees of freedom are combined with distributed charge models for long range electrostatics to describe the interaction energies of condensed phase systems. The main focus is on illustrative examples ranging from pure liquids (dichloromethane, water) to chemically and structurally heterogeneous systems (eutectic liquids, CO on amorphous solid water), reactions (Menshutkin), and spectroscopy (triatomic probes for protein dynamics). For all examples, small to medium-sized clusters are used to represent and improve the total interaction energy compared with reference quantum chemical calculations. Although remarkable accuracy can be achieved for some systems (chemical accuracy for dichloromethane and water), it is clear that more realistic models are required for van der Waals contributions and improved water models need to be used for more quantitative simulations of heterogeneous chemical and biological systems.

physics.chem-ph↗

arXiv2025

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

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 potential energy surface (PES) of CCSD(T) quality, calibrated against the results of new spectroscopic measurements. Second order vibrational perturbation calculations (VPT2) very successfully describe both the framework and H-transfer modes compared with the experiments. In particular, a new low-intensity signature at 1666 cm$^{-1}$ was correctly predicted from the VPT2 calculations. An unstructured band centered at 2940 cm$^{-1}$ superimposed on a broad background extending from 2600 to 3200 cm$^{-1}$ is assigned to the H-transfer motion. The broad background involves a multitude of combination bands but a major role is played by the COH-bend. For the deuterated species, VPT2 and molecular dynamics simulations provide equally convincing assignments, in particular for the framework modes. Finally, based on the new PES the tunneling splitting for H-transfer is predicted as $\Delta_{\rm H} = 35.0$ cm$^{-1}$ from ring polymer instanton calculations using higher-order corrections. This provides an experimentally accessible benchmark to validate the computations, in particular the quality of the machine-learned PES.

physics.chem-ph↗

arXiv2025

End-to-End Photodissociation Dynamics of Energized H$_2$COO

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 hydrogen transfer. From an aggregate of at least 5 $\mu$s of molecular dynamics simulations using a neural network-representation of CASPT2/aug-cc-pVTZ reference data, the branching ratios into molecular products HCO+OH, CO$_2$+H$_2$, or H$_2$O+CO was quantitatively determined. Consistent with earlier calculations and recent experiments, decay into HCO+OH was found to be rare $(\sim 2 \%)$ whereas the other two molecular product channels are accessed with fractions of $\sim 30 \%$ and $\sim 20 \%$, respectively. On the 1 ns time scale, which was the length of an individual MD simulation, more than 40 \% of the systems remain in the reactant state due to partial intramolecular vibrational redistribution (IVR). Formation of CO$_2$+H$_2$ occurs through a bifurcating pathway, one of which passes through formic acid whereas the more probable route connects the di-radical OCH$_2$O with the product through a low-lying transition state. Notably, none of the intermediates along the pathway accumulate and their maximum concentration always remains well below 5 \%. This work demonstrates that atomistic simulations with global reactive machine-learned energy functions provide a quantitative understanding of the chemistry and reaction dynamics for atmospheric reactions in the gas phase.

physics.chem-ph↗

arXiv2025

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

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 based on a combination of machine learning-based and empirical representations of intra- and intermolecular interactions is presented. The total energy is decomposed into internal contributions, and electrostatic and van der Waals interactions between monomers. The monomer potential energy surface is described using a neural network, whereas for the electrostatics the flexible minimally distributed charge model is employed. Remaining contributions between reference energies from electronic structure calculations and the model are fitted to standard Lennard-Jones (12-6) terms. For water as a topical example, reference energies for the monomers are determined from CCSD(T)-F12 calculations whereas for an ensemble of cluster structures containing $[2,60]$ and $[2,4]$ monomers DFT and CCSD(T) energies, respectively, were used to best match the van der Waals contributions. Based on the bulk liquid density and heat of vaporization, the best-performing set of LJ(12-6) parameters was selected and a wide range of condensed phase properties were determined and compared with experiment. MD Simulations on the multiple-nanosecond time scale were carried out for water boxes containing 2000 to 8000 monomers, depending on the property considered. The performance of such a generic ML-inspired parametrization scheme is very promising and future improvements and extensions are discussed, also in view of recent advances for water in particular in the literature.

physics.chem-ph↗