Showing 1–50 of 102 results for author: Meuwly, M

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

Authors: Chao He, Xinyue Zhang, Cangtao Yin, Markus Meuwly, Stefan Willitsch

Abstract: 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… ▽ More 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. △ Less

Submitted 9 July, 2026; originally announced July 2026.

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

Authors: Cangtao Yin, Markus Meuwly

Abstract: 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… ▽ More 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. △ Less

Submitted 7 July, 2026; originally announced July 2026.

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

Authors: Cangtao Yin, Stefan Willitsch, Markus Meuwly

Abstract: 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… ▽ More 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$^1Σ^+$)+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. △ Less

Submitted 14 May, 2026; originally announced May 2026.

Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate

Authors: Chen Qu, Paul L. Houston, Qi Yu, Apurba Nandi, Joel M. Bowman, Valerii Andreichev, Silvan Käser, Markus Meuwly

Abstract: 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… ▽ More 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. △ Less

Submitted 20 April, 2026; v1 submitted 14 April, 2026; originally announced April 2026.

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

Authors: Kham Lek Chaton, Eric D. Boittier, Mike Devereux, Markus Meuwly

Abstract: 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 clu… ▽ More 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. △ Less

Submitted 15 March, 2026; originally announced March 2026.

Towards Quantitative Reaction Dynamics of O3

Authors: Raidel Martin-Barrios, Abhirami Vijayakumar, Jingchun Wang, Markus Meuwly

Abstract: 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-depende… ▽ More 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. △ Less

Submitted 11 March, 2026; originally announced March 2026.

Comments: 25 pages, 5 figures

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

Authors: Valerii Andreichev, Jindra Dušek, Markus Meuwly, Jeremy O. Richardson

Abstract: 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… ▽ More 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. △ Less

Submitted 23 February, 2026; originally announced February 2026.

Efficient, Equivariant Predictions of Distributed Charge Models

Authors: Eric D. Boittier, Markus Meuwly

Abstract: 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 c… ▽ More 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 σ-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). △ Less

Submitted 6 February, 2026; originally announced February 2026.

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

Authors: Cangtao Yin, Markus Meuwly

Abstract: 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/… ▽ More 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 $β$ 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. △ Less

Submitted 17 January, 2026; originally announced January 2026.

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

Authors: Abhirami Vijayakumar, Raidel Martin-Barrios, Markus Meuwly

Abstract: 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 outc… ▽ More 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. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Comments: 33 pages, 11 figures

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

Authors: Cangtao Yin, Meenu Upadhyay, Markus Meuwly

Abstract: 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 temperatur… ▽ More 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). △ Less

Submitted 20 November, 2025; originally announced November 2025.

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

Authors: Valerii Andreichev, Sena Aydin, Kai Töpfer, Markus Meuwly, Luis Itza Vazquez-Salazar

Abstract: 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,… ▽ More 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. △ Less

Submitted 2 November, 2025; originally announced November 2025.

Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions

Authors: Sena Aydin, Valerii Andreichev, Pantelis Maragkoudakis, Markus Meuwly

Abstract: 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 wh… ▽ More 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. △ Less

Submitted 30 October, 2025; originally announced October 2025.

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

Authors: JingChun Wang, Meenu Upadhyay, Eric D. Boittier, Kham Lek Chaton, Valerii Andreichev, Mike Devereux, Shimoni Patel, Sena Aydin, Kai Töpfer, Markus Meuwly

Abstract: 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… ▽ More 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. △ Less

Submitted 15 September, 2025; originally announced September 2025.

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

Authors: Valerii Andreichev, Silvan Käser, Erica L. Bocanegra, Madeeha Salik, Mark A. Johnson, Markus Meuwly

Abstract: 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 framewor… ▽ More 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 $Δ_{\rm H} = 35.0$ 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. △ Less

Submitted 8 August, 2025; originally announced August 2025.

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

Authors: Cangtao Yin, Silvan Käser, Meenu Upadhyay, Markus Meuwly

Abstract: 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 $μ$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… ▽ More 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 $μ$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. △ Less

Submitted 25 July, 2025; originally announced July 2025.

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

Authors: Eric D. Boittier, Silvan Käser, Markus Meuwly

Abstract: 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 de… ▽ More 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. △ Less

Submitted 29 June, 2025; originally announced June 2025.

Reaction Dynamics for the [NNO] System from State-Resolved and Coarse-Grained Models

Authors: Juan Carlos San Vicente Veliz, Sung Min Jo, Jingchun Wang, Raymond J. Bemish, Markus Meuwly

Abstract: The dynamics for the NO($X^2 Π$) + N($^4$S) $\leftrightarrow$ N$_{2}(X^{1}Σ_{g}^{+}$) + O($^{3}$P) reaction was followed in the $^3$A' electronic state using state-to-state (STS) and Arrhenius-based rates from two different high-level potential energy surfaces represented as a reproducing kernel (RKHS) and permutationally invariant polynomials (PIPs). Despite the different number of bound states s… ▽ More The dynamics for the NO($X^2 Π$) + N($^4$S) $\leftrightarrow$ N$_{2}(X^{1}Σ_{g}^{+}$) + O($^{3}$P) reaction was followed in the $^3$A' electronic state using state-to-state (STS) and Arrhenius-based rates from two different high-level potential energy surfaces represented as a reproducing kernel (RKHS) and permutationally invariant polynomials (PIPs). Despite the different number of bound states supported by the RKHS- and PIP-PESs the ignition points from STS and Arrhenius rates are at $\sim 10^{-6}$ s whether or not reverse rates are from assuming microreversibility or explicitly given. Conversion from NO to N$_2$ is incomplete if Arrhenius-rates are used but complete turnover is observed if STS-information is used. This is due to non-equilibrium energy flow and state dynamics which requires a state-based description. Including full dissociation leads asymptotically to the correct 2:1 [N]:[O] concentration with little differences for the species' dynamics depending on the PES used for the STS-information. In conclusion, concentration profiles from coarse-grained simulations are consistent over 14 orders of magnitude in time using STS-information based on two different high-level PESs. △ Less

Submitted 6 June, 2025; originally announced June 2025.

High-Energy Reaction Dynamics of O$_3$

Authors: JingChun Wang, Juan Carlos San Vicente Veliz, Meenu Upadhyay, Markus Meuwly

Abstract: The high-temperature atom exchange and dissociation reaction dynamics of the O($^3$P) + O$_2(^3Σ_g^{-} )$ system are investigated based on a new reproducing kernel-based representation of high-level multi-reference configuration interaction energies. Quasi-classical trajectory (QCT) simulations find the experimentally measured negative tempe-rature-dependence of the rate for the exchange reaction… ▽ More The high-temperature atom exchange and dissociation reaction dynamics of the O($^3$P) + O$_2(^3Σ_g^{-} )$ system are investigated based on a new reproducing kernel-based representation of high-level multi-reference configuration interaction energies. Quasi-classical trajectory (QCT) simulations find the experimentally measured negative tempe-rature-dependence of the rate for the exchange reaction and describe the experiments within error bars. Similarly, QCT simulations for a recent potential energy surface (PES) at a comparable level of quantum chemical theory reproduce the negative $T-$dependence. Interestingly, both PESs feature a ``reef" structure near dissociation which has been implicated to be responsible for a positive $T-$dependence of the rate inconsistent with experiments. For the dissociation reaction the $T-$dependence correctly captures that known from experiments but underestimates the absolute rates by two orders of magnitude. Accounting for an increased number of accessible electronic states reduces this to one order of magnitude. A neural network-based state-to-distribution model is constructed for both PESs and shows good performance in predicting final translational, vibrational, and rotational product state distributions. Such models are valuable for future and more coarse-grained simulations of reactive hypersonic gas flow. △ Less

Submitted 6 June, 2025; originally announced June 2025.

Augmenting chemical databases for atomistic machine learning by sampling conformational space

Authors: Luis Itza Vazquez-Salazar, Markus Meuwly

Abstract: 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, this aspect is investigated for "chemical tasks" including the prediction of hybridization, oxidation, substituent effects, and aromaticity, starting from an initial "restricted" database (iRD). Choosing molecules for… ▽ More 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, this aspect is investigated for "chemical tasks" including the prediction of hybridization, oxidation, substituent effects, and aromaticity, starting from an initial "restricted" database (iRD). Choosing molecules for augmenting this iRD, including increasing numbers of conformations generated at different temperatures, and retraining the models can improve predictions of the models on the selected "tasks". Addition of a small percentage of conformers (1 % ) obtained at 300 K improves the performance in almost all cases. On the other hand, and in line with previous studies, redundancy and highly deformed structures in the augmentation set compromise prediction quality. Energy and bond distributions were evaluated by means of Kullback-Leibler ($D_{\rm KL}$) and Jensen-Shannon ($D_{\rm JS}$) divergence and Wasserstein distance ($W_{1}$). The findings of this work provide a baseline for the rational augmentation of chemical databases or the creation of synthetic databases. △ Less

Submitted 2 April, 2025; originally announced April 2025.

Reaction Dynamics of the H + HeH$^+$ $\rightarrow$ He + H$_2^+$ System

Authors: Meenu Upadhyay, Silvan Käser, Jayakrushna Sahoo, Yohann Scribano, Markus Meuwly

Abstract: The reaction dynamics for the H + HeH$^+$ $\rightarrow$ He + H$_2^+$ reaction in its electronic ground state is investigated using two different representations of the potential energy surface (PES). The first uses a combined kernel and neural network representation of UCCSD(T) reference data whereas the second is a corrected PES (cR-PES) that eliminates an artificial barrier in the entrance chann… ▽ More The reaction dynamics for the H + HeH$^+$ $\rightarrow$ He + H$_2^+$ reaction in its electronic ground state is investigated using two different representations of the potential energy surface (PES). The first uses a combined kernel and neural network representation of UCCSD(T) reference data whereas the second is a corrected PES (cR-PES) that eliminates an artificial barrier in the entrance channel appearing in its initial expansion based on full configuration interaction reference data. Despite the differences between the two PESs, both yield $k_{v=0,j=0} \approx 2 \times 10^{-9}$ cm$^3$/molecule/s at $T = 10$ K which is consistent with a $T-$independent Langevin rate $k_{\rm L} = 2.1 \times 10^{-9}$ cm$^3$/molecule/s but considerably larger than the only experimentally reported value $k_{\rm ICR} = (9.1 \pm 2.5) \times 10^{-10}$ cm$^3$/molecule/s from ion cyclotron resonance experiments. Similarly, branching ratios for the reaction outcomes are comparable for the two PESs. However, when analysing less averaged properties such as initial state-selected $T-$dependent rate coefficients and final vibrational states of the H$_2^+$ product for low temperatures, the differences in the two PESs manifest themselves in the observables. Thus, depending on the property analyzed, accurate and globally valid representations of the PES are required, whereas more approximate and empirical construction schemes can be followed for state-averaged observables. △ Less

Submitted 27 March, 2025; originally announced March 2025.

Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions

Authors: Kai Töpfer, Jingchun Wang, Shimoni Patel, Markus Meuwly

Abstract: Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at the density functional theory level and machine learning-based energy functions for a spectroscopic reporter for eutectic mixtures consisting of water, acetamid… ▽ More Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at the density functional theory level and machine learning-based energy functions for a spectroscopic reporter for eutectic mixtures consisting of water, acetamide and KSCN. Two water models are considered: TIP3P which is consistent with the CGenFF energy function and TIP4P which - as a water model - is superior to TIP4P. Both fitted models, {\bf M2$^{\rm TIP3P}$} and {\bf M2$^{\rm TIP4P}$}, yield favourable thermodynamic, structural, spectroscopic and transport properties from extensive molecular dynamics simulations. In particular, the slow and fast decay times from 2-dimensional infrared spectroscopy and the viscosity for water-rich mixtures are described realistically and consistent with experiments. On the other hand, including the co-solvent (acetamide) in the present case is expected to further improve the computed viscosity for low-water content. It is concluded that such a cluster-based approach is a promising and generalizable route for routine parametrization of heterogeneous, electrostatically dominated systems. △ Less

Submitted 28 February, 2025; originally announced February 2025.

Comments: arXiv admin note: substantial text overlap with arXiv:2408.07638

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

Authors: Silvan Käser, Debasish Koner, Markus Meuwly

Abstract: 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, however, depend directly on the accuracy of the underlying potential energy surface (PES). Guided by the principle of parsimony this work introduces KerNN, a combined kernel/neural network-based approach to represent molecul… ▽ More 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, however, depend directly on the accuracy of the underlying potential energy surface (PES). Guided by the principle of parsimony this work introduces KerNN, a combined kernel/neural network-based approach to represent molecular PESs. Compared to state-of-the-art neural network PESs the number of learnable parameters of KerNN is significantly reduced. This speeds up training and evaluation times by several orders of magnitude while retaining high prediction accuracy. Importantly, using kernels as the features also improves the extrapolation capabilities of KerNN far beyond the coverage provided by the training data which solves a general problem of NN-based PESs. KerNN applied to spectroscopy and reaction dynamics shows excellent performance on test set statistics and observables including vibrational bands computed from classical and quantum simulations. △ Less

Submitted 27 November, 2024; originally announced November 2024.

Machine Learning-Based Enhancements of Empirical Energy Functions: Structure, Dynamics and Spectroscopy of Modified Benzenes

Authors: Kham Lek Chaton, Markus Meuwly

Abstract: The effect of replacing individual contributions to an empirical energy function are assessed for halogenated benzenes (X-Bz, X = H, F, Cl, Br) and chlorinated phenols (Cl-PhOH). Introducing electrostatic models based on distributed charges (MDCM) instead of usual atom-centered point charges yields overestimated hydration free energies unless the van der Waals parameters are reparametrized. Scalin… ▽ More The effect of replacing individual contributions to an empirical energy function are assessed for halogenated benzenes (X-Bz, X = H, F, Cl, Br) and chlorinated phenols (Cl-PhOH). Introducing electrostatic models based on distributed charges (MDCM) instead of usual atom-centered point charges yields overestimated hydration free energies unless the van der Waals parameters are reparametrized. Scaling van der Waals ranges by 10 \% to 20 \% for three Cl-PhOH and most X-Bz yield results within experimental error bars, which is encouraging, whereas for benzene (H-Bz) point charge-based models are sufficient. Replacing the bonded terms by a neural network-trained energy function with either fluctuating charges or MDCM electrostatics also yields qualitatively correct hydration free energies which still require adaptation of the van der Waals parameters. The infrared spectroscopy of Cl-PhOH is rather well predicted by all models although the ML-based energy function performs somewhat better in the region of the framework modes. It is concluded that refinements of empirical energy functions for targeted applications is a meaningful way towards more quantitative simulations. △ Less

Submitted 13 November, 2024; originally announced November 2024.

Diffusion and Spectroscopy of H$_2$ in Myoglobin

Authors: Jiri Käser, Kai Töpfer, Markus Meuwly

Abstract: The diffusional dynamics and vibrational spectroscopy of molecular hydrogen (H$_2$) in myoglobin (Mb) is characterized. Hydrogen has been implicated in a number of physiologically relevant processes, including cellular aging or inflammation. Here, the internal diffusion through the protein matrix was characterized and the vibrational spectroscopy was investigated using conventional empirical energ… ▽ More The diffusional dynamics and vibrational spectroscopy of molecular hydrogen (H$_2$) in myoglobin (Mb) is characterized. Hydrogen has been implicated in a number of physiologically relevant processes, including cellular aging or inflammation. Here, the internal diffusion through the protein matrix was characterized and the vibrational spectroscopy was investigated using conventional empirical energy functions and improved models able to describe higher-order electrostatic moments of the ligand. H$_2$ can occupy the same internal defects as already found for Xe or CO (Xe1 to Xe4 and B-state). Furthermore, 4 additional sites were found, some of which had been discovered in earlier simulation studies. The vibrational spectra using the most refined energy function indicate that depending on the docking site the spectroscopy of H$_2$ differs. The maxima of the absorption spectra cover $\sim 20$ cm$^{-1}$ which are indicative of a pronounced effect of the surrounding protein matrix on the vibrational spectroscopy of the ligand. Electronic structure calculations show that H$_2$ forms a stable complex with the heme-iron (stabilized by $\sim -12$ kcal/mol) but splitting of H$_2$ is unlikely due to a high activation energy ($\sim 50$ kcal/mol). △ Less

Submitted 13 September, 2024; originally announced September 2024.

Energy Relaxation of N$_2$O in Gaseous, Supercritical and Liquid Xenon and SF$_6$

Authors: Kai Töpfer, Shyamsunder Erramilli, Lawrence D. Ziegler, Markus Meuwly

Abstract: Rotational and vibrational energy relaxation (RER and VER) of N$_2$O embedded in xenon and SF$_6$ environments ranging from the gas phase to the liquid, including the supercritical regime, is studied at a molecular level. Calibrated intermolecular interactions from high-level electronic structure calculations, validated against experiments for the pure solvents were used to carry out classical mol… ▽ More Rotational and vibrational energy relaxation (RER and VER) of N$_2$O embedded in xenon and SF$_6$ environments ranging from the gas phase to the liquid, including the supercritical regime, is studied at a molecular level. Calibrated intermolecular interactions from high-level electronic structure calculations, validated against experiments for the pure solvents were used to carry out classical molecular dynamics simulations corresponding to experimental state points for near-critical isotherms. Computed RER rates in low-density solvent of $k_{\rm rot}^{\rm Xe} = (3.67\pm0.25)\cdot10^{10}$ s$^{-1}$M$^{-1}$ and $k_{\rm rot}^{\rm SF_6} = (1.25\pm0.12)\cdot10^{11}$ s$^{-1}$M$^{-1}$ compare well with rates determined by analysis of 2-dimensional infrared experiments. Simulations find that an isolated binary collision (IBC) description is successful up to solvent concentrations of $\sim 4$ M. For higher densities, including the supercritical regime, the simulations do not correctly describe RER, probably due to neglect of solvent-solute coupling in the analysis of the rotational motion. For VER, the near-quantitative agreement between simulations and pump-probe experiments captures the solvent density-dependent trends. △ Less

Submitted 24 October, 2024; v1 submitted 28 August, 2024; originally announced August 2024.

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

Authors: Karl P. Horn, Meenu Upadhyay, Baruch Margulis, Daniel M. Reich, Edvardas Narevicius, Markus Meuwly, Christiane P. Koch

Abstract: High-quality potential energy surfaces (PES) are a prerequisite for quantitative atomistic simulations, with both quantum and classical dynamics approaches. The ultimate test for the validity of a PES are comparisons with judiciously chosen experimental observables. Here we ask whether cold collision measurements are sufficiently informative to validate and distinguish between high-level, state-of… ▽ More High-quality potential energy surfaces (PES) are a prerequisite for quantitative atomistic simulations, with both quantum and classical dynamics approaches. The ultimate test for the validity of a PES are comparisons with judiciously chosen experimental observables. Here we ask whether cold collision measurements are sufficiently informative to validate and distinguish between high-level, state-of-the art PESs for the strongly interacting Ne-H$_2^+$ system. We show that measurement of the final state distributions for a process that involves only several metastable intermediate states is sufficient to identify the PES that captures the long-range interactions properly. Furthermore, we show that a modest increase in the experimental energy resolution will allow for resolving individual Feshbach resonances and enable a quantitative probe of the interactions at short and intermediate range. △ Less

Submitted 28 July, 2025; v1 submitted 23 August, 2024; originally announced August 2024.

Journal ref: J. Phys. Chem. Lett. 2025, 16, XXX, 7862-7867

Force Fields for Deep Eutectic Mixtures: Application to Structure and 2D-Infrared Spectroscopy

Authors: Kai Töpfer, Eric Boittier, Michael Devereux, Andrea Pasti, Peter Hamm, Markus Meuwly

Abstract: Parametrizing energy functions for ionic systems can be challenging. Here, the total energy function for an eutectic system consisting of water, SCN$^-$, K$^+$ and acetamide is improved vis-a-vis experimentally measured properties. Given the importance of electrostatic interactions, two different types of models are considered: the first (model M0) uses atom-centered multipole whereas the other tw… ▽ More Parametrizing energy functions for ionic systems can be challenging. Here, the total energy function for an eutectic system consisting of water, SCN$^-$, K$^+$ and acetamide is improved vis-a-vis experimentally measured properties. Given the importance of electrostatic interactions, two different types of models are considered: the first (model M0) uses atom-centered multipole whereas the other two (models M1 and M2) are based on fluctuating minimal distributed charges (fMDCM) that respond to geometrical changes of SCN$^-$. The Lennard-Jones parameters of the anion are adjusted to best reproduce experimentally known hydration free energies and densities which are matched to within a few percent for the final models irrespective of the electrostatic model. Molecular dynamics simulations of the eutectic mixtures with varying water content (between 0% and 100%) yield radial distribution functions and frequency correlation functions for the CN-stretch vibration. Comparison with experiments indicate that models based on fMDCM are considerably more consistent that those using multipoles. Computed viscosities from models M1 and M2 are within 30% of measured values and their change with increasing water content is consistent with experiments. This is not the case for model M0. △ Less

Submitted 24 October, 2024; v1 submitted 14 August, 2024; originally announced August 2024.

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

Authors: Silvan Käser, Jeremy O. Richardson, Markus Meuwly

Abstract: 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 quantitative characterization of tunneling splittings for system sizes that are currently out of reach using traditional approaches. For intramolecular hydrogen transfer in tropolone, the best computed splitting includi… ▽ More 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 quantitative characterization of tunneling splittings for system sizes that are currently out of reach using traditional approaches. For intramolecular hydrogen transfer in tropolone, the best computed splitting including perturbative corrections in the ring-polymer instanton calculations is 0.94 cm$^{-1}$ and compares with 0.974 cm$^{-1}$ from experiments. On the other hand, for intermolecular double hydrogen transfer in the (propiolic acid)-(formic acid) dimer, the computations yield 0.0147 cm$^{-1}$ which is larger by 40 % compared with experiment (0.0097 cm$^{-1}$) but still in much better agreement than previous attempts (0.63 cm$^{-1}$). The strategy pursued in the present work is applicable to yet larger systems and other properties of interest and provides a rational route for highest-accuracy energy functions for prediction and benchmarking electronic structure methods vis-a-vis experiments. △ Less

Submitted 31 July, 2024; originally announced July 2024.

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

Authors: Kai Töpfer, Luis Itza Vazquez-Salazar, Markus Meuwly

Abstract: 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 physics and chemistry. So far, improvements in the construction of ML-PES models have been conducted independently, creating an initial hurdle for new users to overcome and complicating the reproducibility of results. Aim… ▽ More 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 physics and chemistry. So far, improvements in the construction of ML-PES models have been conducted independently, creating an initial hurdle for new users to overcome and complicating the reproducibility of results. Aiming to reduce the bar for the extensive use of ML-PES, we introduce ${\it Asparagus}$, a software package encompassing the different parts into one coherent implementation that allows an autonomous, user-guided construction of ML-PES models. ${\it Asparagus}$ combines capabilities of initial data sampling with interfaces to ${\it ab initio}$ calculation programs, ML model training, as well as model evaluation and its application within other codes such as ASE or CHARMM. The functionalities of the code are illustrated in different examples, including the dynamics of small molecules, the representation of reactive potentials in organometallic compounds, and atom diffusion on periodic surface structures. The modular framework of ${\it Asparagus}$ is designed to allow simple implementations of further ML-related methods and models to provide constant user-friendly access to state-of-the-art ML techniques. △ Less

Submitted 21 July, 2024; originally announced July 2024.

Kernel-based Minimal Distributed Charges: A Conformationally Dependent ESP-Model for Molecular Simulations

Authors: Eric Boittier, Kai Töpfer, Mike Devereux, Markus Meuwly

Abstract: A kernel-based method (kernelized minimal distributed charge model - kMDCM) to represent the molecular electrostatic potential (ESP) in terms of off-center point charges whose positions adapts to the molecular geometry. Using Gaussian kernels and atom-atom distances as the features, the ESP for water and methanol is shown to improve by at least a factor of two compared with point charge models fit… ▽ More A kernel-based method (kernelized minimal distributed charge model - kMDCM) to represent the molecular electrostatic potential (ESP) in terms of off-center point charges whose positions adapts to the molecular geometry. Using Gaussian kernels and atom-atom distances as the features, the ESP for water and methanol is shown to improve by at least a factor of two compared with point charge models fit to an ensemble of structures. Combining kMDCM for the electrostatics and reproducing kernels for the bonded terms allows energy-conserving simulation of 2000 water molecules with periodic boundary conditions on the nanosecond time scale. △ Less

Submitted 1 June, 2024; originally announced June 2024.

SCN as a Local Probe of Protein Structural Dynamics

Authors: Sena Aydin, Seyedeh Maryam Salehi, Kai Töpfer, Markus Meuwly

Abstract: The dynamics of lysozyme is probed by attaching -SCN to all alanine-residues. The 1-dimensional infrared spectra exhibit frequency shifts in the position of the maximum absorption by 4 cm$^{-1}$ which is consistent with experiments in different solvents and indicates moderately strong interactions of the vibrational probe with its environment. Isotopic substitution $^{12}$C $\rightarrow ^{13}$C le… ▽ More The dynamics of lysozyme is probed by attaching -SCN to all alanine-residues. The 1-dimensional infrared spectra exhibit frequency shifts in the position of the maximum absorption by 4 cm$^{-1}$ which is consistent with experiments in different solvents and indicates moderately strong interactions of the vibrational probe with its environment. Isotopic substitution $^{12}$C $\rightarrow ^{13}$C leads to a red-shift by $-47$ cm$^{-1}$ which is consistent with experiments with results on CN-substituted copper complexes in solution. The low-frequency, far-infrared part of the protein spectra contain label-specific information in the difference spectra when compared with the wild type protein. Depending on the positioning of the labels, local structural changes are observed. For example, introducing the -SCN label at Ala129 leads to breaking of the $α-$helical structure with concomitant change in the far-infrared spectrum. Finally, changes in the local hydration of SCN-labelled Alanine residues as a function of time can be related to angular reorientation of the label. It is concluded that -SCN is potentially useful for probing protein dynamics, both in the high-frequency (CN-stretch) and far-infrared part of the spectrum. △ Less

Submitted 29 April, 2024; originally announced April 2024.

High-Energy Reaction Dynamics of N$_{3}$

Authors: JingChun Wang, Juan Carlos San Vicente Veliz, Markus Meuwly

Abstract: The atom-exchange and atomization dissociation dynamics for the N($^4$S) + N$_2(^1 Σ_{\rm g}^+)$ reaction is studied using a reproducing kernel Hilbert space (RKHS)-based, global potential energy surface (PES) at the MRCI-F12/aug-cc-pVTZ-F12 level of theory. For the atom exchange reaction $({\rm N_A N_B} + {\rm N_C} \rightarrow {\rm N_A N_C} + {\rm N_B}$), computed thermal rates and their temper… ▽ More The atom-exchange and atomization dissociation dynamics for the N($^4$S) + N$_2(^1 Σ_{\rm g}^+)$ reaction is studied using a reproducing kernel Hilbert space (RKHS)-based, global potential energy surface (PES) at the MRCI-F12/aug-cc-pVTZ-F12 level of theory. For the atom exchange reaction $({\rm N_A N_B} + {\rm N_C} \rightarrow {\rm N_A N_C} + {\rm N_B}$), computed thermal rates and their temperature dependence from quasi-classical trajectory (QCT) simulations agree to within error bars with the available experiments. Companion QCT simulations using a recently published CASPT2-based PES confirm these findings. For the atomization reaction, leading to three N$(^4{\rm S})$ atoms, the computed rates from the RKHS-PES overestimate the experimentally reported rates by one order of magnitude whereas those from the PIP-PES agree favourably, and the $T$-dependence of both computations is consistent with experiment. These differences can be traced back to the different methods and basis sets used. The lifetime of the metastable N$_3$ molecule is estimated to be $\sim 200$ fs depending on the initial state of the reactants. Finally, neural network-based exhaustive state-to-distribution models are presented using both PESs for the atom exchange reaction. These models will be instrumental for a broader exploration of the reaction dynamics of air. △ Less

Submitted 29 April, 2024; originally announced April 2024.

CO$_2$ and NO$_2$ Formation on Amorphous Solid Water

Authors: Meenu Upadhyay, Markus Meuwly

Abstract: The dynamics for molecule formation, relaxation, diffusion, and desorption on amorphous solid water is studied in a quantitative fashion. We aim at characterizing, at a quantitative level, the formation probability, stabilization, energy relaxation and diffusion dynamics of CO$_2$ and NO$_2$ on cold amorphous solid water following atom+diatom recombination reactions. Accurate machine-learned energ… ▽ More The dynamics for molecule formation, relaxation, diffusion, and desorption on amorphous solid water is studied in a quantitative fashion. We aim at characterizing, at a quantitative level, the formation probability, stabilization, energy relaxation and diffusion dynamics of CO$_2$ and NO$_2$ on cold amorphous solid water following atom+diatom recombination reactions. Accurate machine-learned energy functions combined with fluctuating charge models were used to investigate the diffusion, interactions, and recombination dynamics of atomic oxygen with CO and NO on amorphous solid water (ASW). Energy relaxation to the ASW and into water-internal-degrees of freedom were determined from analysis of the vibrational density of states. The surface diffusion and desorption energetics was investigated from extended and nonequilibrium MD simulations. The reaction probability on the nanosecond time scale is determined in a quantitative fashion and demonstrates that surface diffusion of the reactants leads to recombination for initial separations up to 20 Å\/. After recombination both, CO$_2$ and NO$_2$, stabilize by energy transfer to water internal and surface phonon modes on the picosecond time scale. The average diffusion barriers and desorption energies agree with those reported from experiments. After recombination, the triatomic products diffuse easily which contrasts with the equilibrium situation in which both, CO$_2$ and NO$_2$, are stationary on the multi-nanosecond time scale. △ Less

Submitted 26 March, 2024; v1 submitted 22 March, 2024; originally announced March 2024.

Comments: 37 pages

Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces

Authors: Luis Itza Vazquez-Salazar, Silvan Käser, Markus Meuwly

Abstract: Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods - Ensembles, Deep Evidential Regression (DER), and Gaussian Mixture Models (GMM) - were applied to the H-transfer reaction between ${\it syn-}$Criegee and vinyl hydroxyperoxide. The results indicate that ensemble models provide the… ▽ More Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods - Ensembles, Deep Evidential Regression (DER), and Gaussian Mixture Models (GMM) - were applied to the H-transfer reaction between ${\it syn-}$Criegee and vinyl hydroxyperoxide. The results indicate that ensemble models provide the best results for detecting outliers, followed by GMM. For example, from a pool of 1000 structures with the largest uncertainty, the detection quality for outliers is $\sim 90$ \% and $\sim 50$ \%, respectively, if 25 or 1000 structures with large errors are sought. On the contrary, the limitations of the statistical assumptions of DER greatly impacted its prediction capabilities. Finally, a structure-based indicator was found to be correlated with large average error, which may help to rapidly classify new structures into those that provide an advantage for refining the neural network. △ Less

Submitted 27 February, 2024; originally announced February 2024.

OH-Formation Following Vibrationally Induced Reaction Dynamics of H$_2$COO

Authors: Kaisheng Song, Meenu Upadhyay, Markus Meuwly

Abstract: The reaction dynamics of H$_2$COO to form linear HCOOH and dioxirane as first steps for OH-elimination is quantitatively investigated. Using a machine learned potential energy surface at the CASPT2/aug-cc-pVTZ level of theory vibrational excitation along the CH-normal mode $ν_{\rm CH}$ with energies up to 40.0 kcal/mol ($\sim 5 ν_{\rm CH}$) leads almost exclusively to linear HCOOH which further de… ▽ More The reaction dynamics of H$_2$COO to form linear HCOOH and dioxirane as first steps for OH-elimination is quantitatively investigated. Using a machine learned potential energy surface at the CASPT2/aug-cc-pVTZ level of theory vibrational excitation along the CH-normal mode $ν_{\rm CH}$ with energies up to 40.0 kcal/mol ($\sim 5 ν_{\rm CH}$) leads almost exclusively to linear HCOOH which further decomposes into OH+HCO. Although the barrier to form dioxirane is only 21.4 kcal/mol the reaction probability to form dioxirane is two orders of magnitude lower if the CH-stretch mode is excited. Following the dioxirane-formation pathway is facile, however, if in addition the COO-bend vibration is excited with energies equivalent to $\sim (2 ν_{\rm CH} + 4 ν_{\rm COO})$ or $\sim (3 ν_{\rm CH} + ν_{\rm COO})$. For OH-formation in the atmosphere the pathway through linear HCOOH is probably most relevant because the alternative pathways (through dioxirane or formic acid) involve several intermediates that can de-excite through collisions, relax {\it via} Intramolecular vibrational energy redistribution (IVR), or pass through very loose and vulnerable transition states (formic acid). This work demonstrates how, by selectively exciting particular vibrational modes, it is possible to dial into desired reaction channels with a high degree of specificity for a process relevant to atmospheric chemistry. △ Less

Submitted 15 February, 2024; originally announced February 2024.

Numerical Accuracy Matters: Applications of Machine Learned Potential Energy Surfaces

Authors: Silvan Käser, Markus Meuwly

Abstract: The role of numerical accuracy in training and evaluating neural network-based potential energy surfaces is examined for different experimental observables. For observables that require third- and fourth-order derivatives of the total energy with respect to Cartesian coordinates single-precision arithmetics as is typically used in ML-based approaches is insufficient and leads to roughness of the u… ▽ More The role of numerical accuracy in training and evaluating neural network-based potential energy surfaces is examined for different experimental observables. For observables that require third- and fourth-order derivatives of the total energy with respect to Cartesian coordinates single-precision arithmetics as is typically used in ML-based approaches is insufficient and leads to roughness of the underlying PES as is explicitly demonstrated. Increasing the numerical accuracy to double-precision yields a smooth PES with higher-order derivatives that are numerically stable and yield meaningful anharmonic frequencies and tunneling splitting as is demonstrated for H$_2$CO and malonaldehyde. For molecular dynamics simulations, which only require first-order derivatives, single-precision arithmetics appears to be sufficient, though. △ Less

Submitted 29 November, 2023; originally announced November 2023.

Systematic Improvement of Empirical Energy Functions in the Era of Machine Learning

Authors: Mike Devereux, Eric D. Boittier, Markus Meuwly

Abstract: The impact of targeted replacement of individual terms in empirical force fields is quantitatively assessed for pure water, dichloromethane (DCM), and solvated K$^+$ and Cl$^-$ ions. For the electrostatics, point charges (PCs) and machine learning (ML)based minimally distributed charges (MDCM) fitted to the molecular electrostatic potential are evaluated together with electrostatics based on the C… ▽ More The impact of targeted replacement of individual terms in empirical force fields is quantitatively assessed for pure water, dichloromethane (DCM), and solvated K$^+$ and Cl$^-$ ions. For the electrostatics, point charges (PCs) and machine learning (ML)based minimally distributed charges (MDCM) fitted to the molecular electrostatic potential are evaluated together with electrostatics based on the Coulomb integral. The impact of explicitly including second-order terms is investigated by adding a fragment molecular orbital (FMO)-derived polarization energy to an existing force field, in this case CHARMM. It is demonstrated that anisotropic electrostatics reduce the RMSE for water (by 1.6 kcal/mol), DCM (by 0.8 kcal/mol) and for solvated Cl$^-$ clusters (by 0.4 kcal/mol). An additional polarization term can be neglected for DCM but notably improves errors in pure water (by 1.1 kcal/mol) and in Cl$^-$ clusters (by 0.4 kcal/mol) and is key to describing solvated K$^+$, reducing the RMSE by 2.3 kcal/mol. A 12-6 Lennard-Jones functional form is found to perform satisfactorily with PC and MDCM electrostatics, but is not appropriate for descriptions that account for the electrostatic penetration energy. The importance of many-body contributions is assessed by comparing a strictly 2-body approach with self-consistent reference data. DCM can be approximated well with a 2-body potential while water and solvated K$^+$ and Cl$^-$ ions require explicit many-body corrections. The present work systematically quantifies which terms improve the performance of an existing force field and what reference data to use for parametrizing these terms in a tractable fashion for ML fitting of pure and heterogeneous systems. △ Less

Submitted 28 October, 2023; originally announced October 2023.

Improving Potential Energy Surfaces Using Experimental Feshbach Resonance Tomography

Authors: Karl P. Horn, Luis Itza Vazquez-Salazar, Christiane P. Koch, Markus Meuwly

Abstract: The structure and dynamics of a molecular system is governed by its potential energy surface (PES), representing the total energy as a function of the nuclear coordinates. Obtaining accurate potential energy surfaces is limited by the exponential scaling of Hilbert space, restricting quantitative predictions of experimental observables from first principles to small molecules with just a few elect… ▽ More The structure and dynamics of a molecular system is governed by its potential energy surface (PES), representing the total energy as a function of the nuclear coordinates. Obtaining accurate potential energy surfaces is limited by the exponential scaling of Hilbert space, restricting quantitative predictions of experimental observables from first principles to small molecules with just a few electrons. Here, we present an explicitly physics-informed approach for improving and assessing the quality of families of PESs by modifying them through linear coordinate transformations based on experimental data. We demonstrate this "morphing" of the PES for the He-H$_{2}^{+}$ complex for reference surfaces at three different levels of quantum chemistry and using recent comprehensive Feshbach resonance(FR) measurements. In all cases, the positions and intensities of peaks in the collision cross-section are improved. We find these observables to be mainly sensitive to the long-range part of the PES. △ Less

Submitted 28 September, 2023; originally announced September 2023.

On the Effect of Aleatoric and Epistemic Errors on the Learnability and Quality of NN-based Potential Energy Surfaces

Authors: S. Goswami, S. Käser, R. J. Bemish, M. Meuwly

Abstract: 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 result from aleatoric and epistemic errors in the quantum chemical reference calculations. Statistical (aleatoric) noise arises for example due to the need to set convergence thresholds in the self consistent field (SCF… ▽ More 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 result from aleatoric and epistemic errors in the quantum chemical reference calculations. Statistical (aleatoric) noise arises for example due to the need to set convergence thresholds in the self consistent field (SCF) iterations whereas systematic (epistemic) noise is due to, {\it inter alia}, particular choices of basis sets in the calculations. The two molecules considered here as proxies are H$_{2}$CO and HONO which are examples for single- and multi-reference problems, respectively, for geometries around the minimum energy structure. For H$_2$CO it is found that adding noise to energies with magnitudes representative of single-point calculations does not deteriorate the quality of the final PESs whereas increasing the noise level commensurate with electronic structure calculations for more complicated, e.g. metal-containing, systems is expected to have a more notable effect. However, the effect of noise on the forces is more noticeable. On the other hand, for HONO which requires a multi-reference treatment, a clear correlation between model quality and the degree of multi-reference character as measured by the $T_1$ amplitude is found. It is concluded that for chemically "simple" cases the effect of aleatoric and epistemic noise is manageable without evident deterioration of the trained model - although the quality of the forces is important. However, considerably more care needs to be exercised for situations in which multi-reference effects are present. △ Less

Submitted 10 September, 2023; originally announced September 2023.

Molecular Simulation for Atmospheric Reaction Exploration and Discovery: Non-Equilibrium Dynamics, Roaming and Glycolaldehyde Formation Following Photo-Induced Decomposition of syn-Acetaldehyde Oxide

Authors: Meenu Upadhyay, Kai Töpfer, Markus Meuwly

Abstract: The decomposition and chemical dynamics for vibrationally excited syn-CH$_3$CHOO is followed based on statistically significant numbers of molecular dynamics simulations. Using a neural network-based reactive potential energy surface, transfer learned to the CASPT2 level of theory, the final total kinetic energy release and rotational state distributions of the OH fragment are in quantitative agre… ▽ More The decomposition and chemical dynamics for vibrationally excited syn-CH$_3$CHOO is followed based on statistically significant numbers of molecular dynamics simulations. Using a neural network-based reactive potential energy surface, transfer learned to the CASPT2 level of theory, the final total kinetic energy release and rotational state distributions of the OH fragment are in quantitative agreement with experiment. In particular the widths of these distributions are sensitive to the experimentally unknown strength of the O--O bond strength, for which values $D_e \in [22,25]$ kcal/mol are found. Due to the non-equilibrium nature of the process considered, the energy-dependent rates do not depend appreciably on the O--O scission energy. Roaming dynamics of the OH-photoproduct leads to formation of glycolaldehyde on the picosecond time scale with subsequent decomposition into CH$_2$OH+HCO. Atomistic simulations with global reactive machine-learned energy functions provide a viable route to quantitatively explore the chemistry and reaction dynamics for atmospheric reactions. △ Less

Submitted 6 July, 2023; originally announced July 2023.

Comments: 28+11pages, 7+14figures

Low-Temperature Kinetics for the N + NO reaction: Experiment Guides the Way

Authors: Kevin M. Hickson, Juan Carlos San Vicente Veliz, Debasish Koner, Markus Meuwly

Abstract: The reaction N(4S) + NO -> O(3P) + N2 plays a pivotal role in the conversion of atomic to molecular nitrogen in dense interstellar clouds and in the atmosphere. Here we report a joint experimental and computational investigation of the N + NO reaction with the aim of providing improved constraints on its low temperature reactivity. Thermal rates were measured over the 50 to 296 K range in a contin… ▽ More The reaction N(4S) + NO -> O(3P) + N2 plays a pivotal role in the conversion of atomic to molecular nitrogen in dense interstellar clouds and in the atmosphere. Here we report a joint experimental and computational investigation of the N + NO reaction with the aim of providing improved constraints on its low temperature reactivity. Thermal rates were measured over the 50 to 296 K range in a continuous supersonic flow reactor coupled with pulsed laser photolysis and laser induced fluorescence for the production and detection of N(4S) atoms, respectively. With decreasing temperature, the experimentally measured reaction rate was found to monotonously increase up to a value of (6.6 +- 1.3) x 10-11 cm3 s-1 at 50 K. To confirm this finding, quasi-classical trajectory simulations were carried out on a previously validated, full-dimensional potential energy surface (PES). However, around 50 K the computed rates decreased which required re-evaluation of the reactive PES in the long-range part due to a small spurious barrier with height 40 K in the entrance channel. By exploring different correction schemes the measured thermal rates can be adequately reproduced, displaying a clear negative temperature dependence over the entire temperature range. The possible astrochemical implications of an increased reaction rate at low temperature are also discussed. △ Less

Submitted 9 May, 2023; originally announced May 2023.

Comments: 28 pages, 6 figures and 2 tables in the main article. 3 figures and 1 table in the supplementary information. Accepted for publication in PCCP

PhysNet Meets CHARMM: A Framework for Routine Machine Learning / Molecular Mechanics Simulations

Authors: Kaisheng Song, Silvan Käser, Kai Töpfer, Luis Itza Vazquez-Salazar, Markus Meuwly

Abstract: 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-phase for various experimental observables ranging from spectroscopy to reaction dynamics. Here, the MLpot extension with PhysNet as the ML-based model for a PES is introduced into the newly developed pyCHARMM API. To i… ▽ More 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-phase for various experimental observables ranging from spectroscopy to reaction dynamics. Here, the MLpot extension with PhysNet as the ML-based model for a PES is introduced into the newly developed pyCHARMM API. To illustrate conceiving, validating, refining and using a typical workflow, para-chloro-phenol is considered as an example. The main focus is on how to approach a concrete problem from a practical perspective and applications to spectroscopic observables and the free energy for the -OH torsion in solution are discussed in detail. For the computed IR spectra in the fingerprint region the computations for para-chloro-phenol in water are in good qualitative agreement with experiment carried out in CCl$_4$. Also, relative intensities are largely consistent with experimental findings. The barrier for rotation of the -OH group increases from $\sim 3.5$ kcal/mol in the gas phase to $\sim 4.1$ kcal/mol from simulations in water due to favourable H-bonding interactions of the -OH group with surrounding water molecules. △ Less

Submitted 25 April, 2023; originally announced April 2023.

Comments: 38 pages, 11 figures

Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions

Authors: Ardita Kilaj, Silvan Käser, Jia Wang, Patrik Straňák, Max Schwilk, Lei Xu, O. Anatole von Lilienfeld, Jochen Küpper, Markus Meuwly, Stefan Willitsch

Abstract: Recent advances in experimental methodology enabled studies of the quantum-state and conformational dependence of chemical reactions under precisely controlled conditions in the gas phase. Here, we generated samples of selected gauche and s-trans 2,3-dibromobutadiene (DBB) by electrostatic deflection in a molecular beam and studied their reaction with Coulomb crystals of laser-cooled… ▽ More Recent advances in experimental methodology enabled studies of the quantum-state and conformational dependence of chemical reactions under precisely controlled conditions in the gas phase. Here, we generated samples of selected gauche and s-trans 2,3-dibromobutadiene (DBB) by electrostatic deflection in a molecular beam and studied their reaction with Coulomb crystals of laser-cooled $\mathrm{Ca^{+}}$ ions in an ion trap. The rate coefficients for the total reaction were found to strongly depend on both the conformation of DBB and the electronic state of $\mathrm{Ca^{+}}$. In the $\mathrm{(4p)~^{2}P_{1/2}}$ and $\mathrm{(3d)~^{2}D_{3/2}}$ excited states of $\mathrm{Ca^{+}}$, the reaction is capture-limited and faster for the gauche conformer due to long-range ion-dipole interactions. In the $\mathrm{(4s)~^{2}S_{1/2}}$ ground state of $\mathrm{Ca^{+}}$, the reaction rate for s-trans DBB still conforms with the capture limit, while that for gauche DBB is strongly suppressed. The experimental observations were analysed with the help of adiabatic capture theory, ab-initio calculations and reactive molecular dynamics simulations on a machine-learned full-dimensional potential energy surface of the system. The theory yields near-quantitative agreement for s-trans-DBB, but overestimates the reactivity of the gauche-conformer compared to the experiment. The present study points to the important role of molecular geometry even in strongly reactive exothermic systems and illustrates striking differences in the reactivity of individual conformers in gas-phase ion-molecule reactions. △ Less

Submitted 21 March, 2023; originally announced March 2023.

Transfer-Learned Potential Energy Surfaces: Towards Microsecond-Scale Molecular Dynamics Simulations in the Gas Phase at CCSD(T) Quality

Authors: Silvan Käser, Markus Meuwly

Abstract: The rise of machine learning has greatly influenced the field of computational chemistry, and that of atomistic molecular dynamics simulations in particular. One of its most exciting prospects is the development of accurate, full-dimensional potential energy surfaces (PESs) for molecules and clusters, which, however, often require thousands to tens of thousands of ab initio data points restricting… ▽ More The rise of machine learning has greatly influenced the field of computational chemistry, and that of atomistic molecular dynamics simulations in particular. One of its most exciting prospects is the development of accurate, full-dimensional potential energy surfaces (PESs) for molecules and clusters, which, however, often require thousands to tens of thousands of ab initio data points restricting the community to medium sized molecules and/or lower levels of theory (e.g. DFT). Transfer learning, which improves a global PES from a lower to a higher level of theory, offers a data efficient alternative requiring only a fraction of the high level data (on the order of 100 are found to be sufficient for malonaldehyde). The present work demonstrates that even with Hartree-Fock theory and a double-zeta basis set as the lower level model, transfer learning yields CCSD(T)-level quality for H-transfer barrier energies, harmonic frequencies and H-transfer tunneling splittings. Most importantly, finite-temperature molecular dynamics simulations on the sub-microsecond time scale in the gas phase are possible and the infrared spectra determined from the transfer learned PESs are in good agreement with experiment. It is concluded that routine, long-time atomistic simulations on PESs fulfilling CCSD(T)-standards become possible. △ Less

Submitted 21 March, 2023; originally announced March 2023.

Molecular-Level Understanding of the Ro-vibrational Spectra of N$_2$O in Gaseous, Supercritical and Liquid SF$_6$ and Xe

Authors: Kai Töpfer, Debasish Koner, Shyamsunder Erramilli, Lawrence D. Ziegler, Markus Meuwly

Abstract: The transition between the gas-, supercritical-, and liquid-phase behaviour is a fascinating topic which still lacks molecular-level understanding. Recent ultrafast two-dimensional infrared spectroscopy experiments suggested that the vibrational spectroscopy of N$_2$O embedded in xenon and SF$_6$ as solvents provides an avenue to characterize the transitions between different phases as the concent… ▽ More The transition between the gas-, supercritical-, and liquid-phase behaviour is a fascinating topic which still lacks molecular-level understanding. Recent ultrafast two-dimensional infrared spectroscopy experiments suggested that the vibrational spectroscopy of N$_2$O embedded in xenon and SF$_6$ as solvents provides an avenue to characterize the transitions between different phases as the concentration (or density) of the solvent increases. The present work demonstrates that classical molecular dynamics simulations together with accurate interaction potentials allows to (semi-)quantitatively describe the transition in rotational vibrational infrared spectra from the P-/R-branch lineshape for the stretch vibrations of N$_2$O at low solvent densities to the Q-branch-like lineshapes at high densities. The results are interpreted within the classical theory of rigid-body rotation in more/less constraining environments at high/low solvent densities or based on phenomenological models for the orientational relaxation of rotational motion. It is concluded that classical MD simulations provide a powerful approach to characterize and interpret the ultrafast motion of solutes in low to high density solvents at a molecular level. △ Less

Submitted 21 March, 2023; v1 submitted 14 February, 2023; originally announced February 2023.

Water Dynamics around T0 vs. R4 of Hemoglobin from Local Hydrophobicity Analysis

Authors: Seyedeh Maryam Salehi, Marco Pezzella, Adam Willard, Markus Meuwly, Martin Karplus

Abstract: The local hydration around tetrameric Hb in its T$_0$ and R$_4$ conformational substates is analyzed based on molecular dynamics simulations. Analysis of the local hydrophobicity (LH) for all residues at the $α_1 β_2$ and $α_2 β_1$ interfaces, responsible for the quaternary T$\rightarrow$R transition, which is encoded in the MWC model, as well as comparison with earlier computations of the solvent… ▽ More The local hydration around tetrameric Hb in its T$_0$ and R$_4$ conformational substates is analyzed based on molecular dynamics simulations. Analysis of the local hydrophobicity (LH) for all residues at the $α_1 β_2$ and $α_2 β_1$ interfaces, responsible for the quaternary T$\rightarrow$R transition, which is encoded in the MWC model, as well as comparison with earlier computations of the solvent accessible surface area (SASA), makes clear that the two quantities measure different aspects of hydration. Local hydrophobicity quantifies the presence and structure of water molecules at the interface whereas ``buried surface'' reports on the available space for solvent. For simulations with Hb frozen in its T$_0$ and R$_4$ states the correlation coefficient between LH and buried surface is 0.36 and 0.44, respectively, but it increases considerably if the 95 \% confidence interval is used. The LH with Hb frozen and flexible changes little for most residues at the interfaces but is significantly altered for a few select ones, which are Thr41$α$, Tyr42$α$, Tyr140$α$, Trp37$β$, Glu101$β$ (for T$_0$) and Thr38$α$, Tyr42$α$, Tyr140$α$ (for R$_4$). The number of water molecules at the interface is found to increase by $\sim 25$ \% for T$_0$$\rightarrow$R$_4$ which is consistent with earlier measurements. Since hydration is found to be essential to protein function, it is clear that hydration also plays an essential role in allostery. △ Less

Submitted 8 December, 2022; originally announced December 2022.

Tomography of Feshbach Resonance States

Authors: Baruch Margulis, Karl P. Horn, Daniel M. Reich, Meenu Upadhyay, Nitzan Kahn, Arthur Christianen, Ad van der Avoird, Gerrit C. Groenenboom, Markus Meuwly, Christiane P. Koch, Edvardas Narevicius

Abstract: Feshbach resonances are fundamental to interparticle interactions and become particularly important in cold collisions with atoms, ions, and molecules. Here we present the detection of Feshbach resonances in a benchmark system for strongly interacting and highly anisotropic collisions -- molecular hydrogen ions colliding with noble gas atoms. The collisions are launched by cold Penning ionization… ▽ More Feshbach resonances are fundamental to interparticle interactions and become particularly important in cold collisions with atoms, ions, and molecules. Here we present the detection of Feshbach resonances in a benchmark system for strongly interacting and highly anisotropic collisions -- molecular hydrogen ions colliding with noble gas atoms. The collisions are launched by cold Penning ionization exclusively populating Feshbach resonances that span both short- and long-range parts of the interaction potential. We resolved all final molecular channels in a tomographic manner using ion-electron coincidence detection. We demonstrate the non-statistical nature of the final state distribution. By performing quantum scattering calculations on ab initio potential energy surfaces, we show that the isolation of the Feshbach resonance pathways reveals their distinctive fingerprints in the collision outcome. △ Less

Submitted 9 March, 2023; v1 submitted 6 December, 2022; originally announced December 2022.

Journal ref: Science 380, 77-81 (2023)

Neural Network Potentials for Chemistry: Concepts, Applications and Prospects

Authors: Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly, Kai Töpfer

Abstract: Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying conce… ▽ More Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on larger scale. △ Less

Submitted 22 December, 2022; v1 submitted 23 September, 2022; originally announced September 2022.

Local Hydration Control and Functional Implications Through S-Nitrosylation of Proteins: Kirsten rat sarcoma virus (KRAS) and Hemoglobin (Hb)

Authors: Haydar Taylan Turan, Markus Meuwly

Abstract: S-nitrosylation, the covalent addition of NO to the thiol side chain of cysteine, is an important post-transitional modification (PTM) that can affect the function of proteins. As such, PTMs extend and diversify protein functions and thus characterizing consequences of PTM at a molecular level is of great interest. Although PTMs can be detected through various direct/indirect methods, they lack th… ▽ More S-nitrosylation, the covalent addition of NO to the thiol side chain of cysteine, is an important post-transitional modification (PTM) that can affect the function of proteins. As such, PTMs extend and diversify protein functions and thus characterizing consequences of PTM at a molecular level is of great interest. Although PTMs can be detected through various direct/indirect methods, they lack the capabilities to investigate the modifications at the molecular level. In the present work local and global structural dynamics, their correlation, the hydration structure, and the infrared spectroscopy for WT and S-nitrosylated Kirsten rat sarcoma virus (KRAS) and Hemoglobin (Hb) are characterized from molecular dynamics simulations. It is found that for KRAS attaching NO to Cys118 rigidifies the protein in the Switch-I region which has functional implications, whereas for Hb nitrosylation at Cys93 at the $β_1$ chain increases the flexibility of secondary structural motives for Hb in its T$_{0}$ and R$_{4}$ conformational substates. Solvent water access decreased by 40\% after nitrosylation in KRAS, similar to Hb for which, however, local hydration of the R$_4$NO state is yet lower than for T$_0$NO. Finally, S-nitrosylation leads to detectable peaks for NO stretch, however, congested IR region will make experimental detection of these bands difficult. △ Less

Submitted 10 September, 2022; originally announced September 2022.