arXiv subjects

Kai Töpfer

Publications and source records attributed to Kai Töpfer.

17 recordsLinked to original sources

arXiv2026

A Machine-Learned Symbolic Committor for a Chemical Reaction: Retinal Isomerization

The thermal cis-trans isomerization around the C$_{13}$=C$_{14}$ double bond of retinal is a prototypical high-barrier reaction whose mechanism hinges on subtle out-of-plane bending motions. We apply Artificial Intelligence for Molecular Mechanism Discovery (AIMMD) to N-retinylidene-lysine in vacuum, learning the committor from unbiased molecular dynamics trajectories generated by two-way shooting. Parametrizing the logit of the committor, rather than the committor itself, allows the neural network to resolve the reaction coordinate across the full transition region, not only at the isocommittor surface $p_B(\mathbf{x}) = 0.5$. Holdback input randomization identifies four proper dihedrals around the reactive bond as the informative coordinates, while the improper dihedrals at C$_{13}$ and C$_{14}$ prove unsuitable because reactant, transition, and product states share the same values. Symbolic regression then distills the network into compact analytical expressions and shows that a nonlinear coupling of all four dihedrals is required to reproduce the S-shaped, stepwise pathway seen in the transition path ensemble. This S-shape is absent from the minimum-free-energy path: it arises from the non-equilibrium dynamics of the short ($\sim 0.13$ ps) transition events combined with the mass asymmetry between heavy-atom and hydrogen-bearing dihedrals. An interpretable, machine-learned committor thus exposes dynamical features of the mechanism to which the free-energy surface is blind. The workflow requires no prior assumptions about the reaction coordinate and extends naturally to other isomerizations and to chemical reactions more broadly.

physics.chem-ph↗

arXiv2025

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

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

physics.chem-ph↗

arXiv2025

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

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

physics.chem-ph↗

arXiv2025

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

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.

physics.chem-ph↗

arXiv2024

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

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.

physics.chem-ph↗

arXiv2024

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

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.

physics.chem-ph↗

arXiv2024

Diffusion and Spectroscopy of H$_2$ in Myoglobin

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).

physics.chem-ph↗

arXiv2024

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

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.

physics.chem-ph↗

arXiv2024

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

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.

physics.chem-ph↗

arXiv2024

SCN as a Local Probe of Protein Structural Dynamics

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.

physics.chem-ph↗

arXiv2023

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

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.

physics.chem-ph↗

arXiv2023

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

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.

physics.chem-ph↗

arXiv2023

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

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.

physics.chem-ph↗

arXiv2022

Neural Network Potentials for Chemistry: Concepts, Applications and Prospects

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.

physics.chem-ph↗

arXiv2022

Structure, Organization and Heterogeneity of Water-Containing Deep Eutectic Solvents

The spectroscopy and structural dynamics of a deep eutectic mixture (KSCN/acetamide) with varying water content is investigated from 2D IR (with the C-N stretch vibration of the SCN$^-$ anions as the reporter) and THz spectroscopy. Molecular dynamics simulations correctly describe the non-trivial dependence of both spectroscopic signatures depending on water content. For the 2D IR spectra, the MD simulations relate the steep increase in the cross relaxation rate at high water content to parallel alignment of packed SCN$^-$ anions. Conversely, the non-linear increase of the THz absorption with increasing water content is mainly attributed to the formation of larger water clusters. The results demonstrate that a combination of structure sensitive spectroscopies and molecular dynamics simulations provides molecular-level insights into emergence of heterogeneity of such mixtures by modulating their composition.

physics.chem-ph↗

arXiv2022

Hydration Dynamics and IR Spectroscopy of 4-Fluorophenol

Halogenated groups are relevant in pharmaceutical applications and potentially useful spectroscopic probes for infrared spectroscopy. In this work, the structural dynamics and infrared spectroscopy of $para$-fluorophenol (F-PhOH) and phenol (PhOH) is investigated in the gas phase and in water using a combination of experiment and molecular dynamics (MD) simulations. The gas phase and solvent dynamics around F-PhOH and PhOH is characterized from atomistic simulations using empirical energy functions with point charges or multipoles for the electrostatics, Machine-Learning (ML) based parametrization and with full $\textit{ab initio}$ (QM) and mixed Quantum Mechanical/Molecular Mechanics (QM/MM) simulations with a particular focus on the CF- and OH-stretch region. The CF-stretch band is heavily mixed with other modes whereas the OH-stretch in solution displays a characteristic high-frequency peak around 3600 cm$^{-1}$ most likely associated with the -OH group of PhOH and F-PhOH together with a characteristic progression below 3000 cm$^{-1}$ due to coupling with water modes which is also reproduced by several of the simulations. Solvent and radial distribution functions indicate that the CF-site is largely hydrophobic except for simulations using point charges which renders them unsuited for correctly describing hydration and dynamics around fluorinated sites.

physics.chem-ph↗

arXiv2022

Double Proton Transfer in Hydrated Formic Acid Dimer: Interplay of Spatial Symmetry and Solvent-Generated Force on Reactivity

The double proton transfer (DPT) reaction in hydrated formic acid dimer (FAD) is investigated at molecular-level detail. For this, a global and reactive machine learned (ML) potential energy surface (PES) is developed to run extensive (more than 100 ns) mixed ML/MM molecular dynamics (MD) simulations in explicit molecular mechanics (MM) solvent at MP2-quality for the solute. Simulations with fixed - as in a conventional empirical force field - and conformationally fluctuating - as available from the ML-based PES - charge models for FAD shows significant impact on the competition between DPT and dissociation of FAD into two formic acid monomers. With increasing temperature the barrier height for DPT in solution changes by about 10% ($\sim 1$ kcal/mol) between 300 K and 600 K. The rate for DPT is largest, $\sim 1$ ns$^{-1}$, at 350 K and decreases for higher temperatures due to destabilisation and increased probability for dissociation of FAD. The water solvent is found to promote the first proton transfer by exerting a favourable solvent-induced Coulomb force along the O-H$\cdots$O hydrogen bond whereas the second proton transfer is significantly controlled by the O-O separation and other conformational degrees of freedom. Double proton transfer in hydrated FAD is found to involve a subtle interplay and balance between structural and electrostatic factors.

physics.chem-ph↗