arXiv subjects

Ondrej Marsalek

Publications and source records attributed to Ondrej Marsalek.

At least 19 recordsLinked to original sources

arXiv2026

How Alkali Metal Cations Affect the Structure and Reactivity of the Hydrated Dielectron

Hydrated electrons of opposite spins pair to form dielectrons at sufficiently high concentrations that can be achieved by dissolution of alkali metals in water. While experimental investigations of these systems are challenging due to their vigorous, even explosive, reactivity, simulations open the possibility to characterize the structure and reactivity of hydrated dielectrons and the effects of alkali cations thereon. Here, we present ab initio molecular dynamics simulations of a hydrated dielectron without or with explicit Li$^+$ or Cs$^+$ counterions. While the overall solvation structure is preserved in all these systems, the presence of cations has a distinct effect of increasing the dielectron gyration radius by about 10% and forming cation-specific cation-dielectron arrangements. Moreover, analysis of water bond lengths reveals in all studied systems a substantial elongation of first-shell O-H bonds oriented toward the dielectron, providing a structural explanation for vibrational red-shifts observed in resonance Raman measurements. Finally, at the 10 ps simulation timescale, rare reactive events were observed, albeit only for the system without metal cations, where hydride intermediates stable on picosecond timescales were identified. These observations also suggest that on the investigated timescales, metal cations may suppress hydrated dielectron reactivity.

physics.chem-ph↗

arXiv2026

More converged, less accurate? Reassessing standard choices for ab initio water using machine learning potentials

Accurately simulating the properties of liquid water remains a central challenge in molecular simulations. In this work, we use machine learning potentials to investigate how the convergence settings of electronic structure calculations impact the predicted structural and dynamical properties of simulated water and ice. We evaluate the true performance of several reference methods in classical and path-integral molecular dynamics. When we compare a popular, computationally pragmatic revPBE0-D3 setup against a highly converged one, our results reveal that its widely reported experimental agreement degrades. Applying the same highly converged settings to the $\mathrmω$B97X-rV functional, we find an improved agreement with experimental results. MP2 with a triple-$ζ$ basis set commonly used for liquid water shows poor performance, which is indicative of insufficient convergence. These findings underscore the need for fully converged reference calculations when evaluating the fundamental accuracy of electronic structure methods and developing reliable models for aqueous systems.

physics.chem-ph↗

arXiv2025

Multi-head committees enable direct uncertainty prediction for atomistic foundation models

Machine learning potentials have become a standard tool for atomistic materials modelling. While models continue to become more generalisable, an open challenge relates to efficient uncertainty predictions for active learning and robust error analysis. In this work, we utilise MACE and its multi-head mechanism to implement a committee neural network potential for message-passing architectures, where the committee comprises multiple output modules attached to the same atomic environment descriptors. As with traditional committees of independent networks, the standard deviation of the predictions functions as an estimate of the model's uncertainty. We show for a range of datasets in custom-build models that the uncertainty of the force predictions correlates well with the true errors. We subsequently apply this concept to foundation models, specifically MACE-MP-0, where we train only the newly attached output heads while keeping the remaining part of the model fixed. We use this approach in an active learning workflow to condense the training set of the foundation model to just 5\% of its original size. The foundation model multi-head committee trained on the condensed training set enables reliable uncertainty estimation without any substantial decrease in prediction accuracy.

physics.chem-ph↗

arXiv2024

Elucidating the Nature of $π$-hydrogen Bonding in Liquid Water and Ammonia

Aromatic compounds form an unusual kind of hydrogen bond with water and ammonia molecules, known as the $π$-hydrogen bond. In this work, we report ab initio path integral molecular dynamics simulations enhanced by machine-learning potentials to study the structural, dynamical, and spectroscopic properties of solutions of benzene in liquid water and ammonia. Specifically, we model the spatial distribution functions of the solvents around the benzene molecule, establish the $π$-hydrogen bonding interaction as a prominent structural motive, and set up existence criteria to distinguish the $π$-hydrogen bonded configurations. These serve as a structural basis to calculate binding affinities of the solvent molecules in $π$hydrogen bonds, identify an anticooperativity effect across the aromatic ring in water (but not ammonia), and estimate $π$-hydrogen bond lifetimes in both solvents. Finally, we model hydration-shell-resolved vibrational spectra to clearly identify the vibrational signature of this structural motif in our simulations. These decomposed spectra corroborate previous experimental findings for benzene in water, offer additional insights, and further emphasize the contrast between $π$-hydrogen bonds in water and in ammonia. Our simulations provide a comprehensive picture of the studied phenomenon and, at the same time, serve as a meaningful \textit{ab initio} reference for an accurate description of $π$-hydrogen bonding using empirical force fields in more complex situations, such as the hydration of biological interfaces.

physics.chem-ph↗

arXiv2023

Developing machine-learned potentials to simultaneously capture the dynamics of excess protons and hydroxide ions in classical and path integral simulations

The transport of excess protons and hydroxide ions in water underlies numerous important chemical and biological processes. Accurately simulating the associated transport mechanisms ideally requires utilizing ab initio molecular dynamics simulations to model the bond breaking and formation involved in proton transfer and path-integral simulations to model the nuclear quantum effects relevant to light hydrogen atoms. These requirements result in a prohibitive computational cost, especially at the time and length scales needed to converge proton transport properties. Here, we present machine-learned potentials (MLPs) that can model both excess protons and hydroxide ions at the generalized gradient approximation and hybrid density functional theory levels of accuracy and use them to perform multiple nanoseconds of both classical and path-integral proton defect simulations at a fraction of the cost of the corresponding ab initio simulations. We show that the MLPs are able to reproduce ab initio trends and converge properties such as the diffusion coefficients of both excess protons and hydroxide ions. We use our multi-nanosecond simulations, which allow us to monitor large numbers of proton transfer events, to analyze the role of hypercoordination in the transport mechanism of the hydroxide ion and provide further evidence for the asymmetry in diffusion between excess protons and hydroxide ions.

physics.chem-ph↗

arXiv2023

Reducing the cost of neural network potential generation for reactive molecular systems

Although machine-learning potentials have recently had substantial impact on molecular simulations, the construction of a robust training set can still become a limiting factor, especially due to the requirement of a reference ab initio simulation that covers all the relevant geometries of the system. Recognizing that this can be prohibitive for certain systems, we develop the method of transition tube sampling that mitigates the computational cost of training set and model generation. In this approach, we generate classical or quantum thermal geometries around a transition path describing a conformational change or a chemical reaction using only a sparse set of local normal mode expansions along this path and select from these geometries by an active learning protocol. This yields a training set with geometries that characterize the whole transition without the need for a costly reference trajectory. The performance of the method is evaluated on different molecular systems with the complexity of the potential energy landscape increasing from a single minimum to a double proton-transfer reaction with high barriers. Our results show that the method leads to training sets that give rise to models applicable in classical and path integral simulations alike that are on par with those based directly on ab initio calculations while providing the computational speed-up we have come to expect from machine-learning potentials.

physics.chem-ph↗

arXiv2021

Electronic Structure of the Solvated Benzene Radical Anion

The benzene radical anion is a molecular ion pertinent to several organic reactions, including the Birch reduction of benzene in liquid ammonia. The species exhibits a dynamic Jahn-Teller effect due to its open-shell nature and undergoes pseudorotation of its geometry. Here we characterize the complex electronic structure of this condensed-phase system based on ab initio molecular dynamics simulations and GW calculations of the benzene radical anion solvated in liquid ammonia. Using detailed analysis of molecular and electronic structure, we find that the spatial character of the excess electron of the solvated radical anion follows the underlying Jahn-Teller distortions of the molecular geometry. We decompose the electronic density of states to isolate the contribution of the solute and to examine the response of the solvent to its presence. Our findings show the correspondence between instantaneous molecular structure and spin density, provide important insights into the electronic stability of the species, revealing that it is indeed a bound state in the condensed phase, and offer electronic densities of states that aid in the interpretation of experimental photoelectron spectra.

physics.chem-ph↗

arXiv2021

Machine learning potentials for complex aqueous systems made simple

Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as solid-liquid interfaces. Here, we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally-optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterwards be applied in exhaustive simulations to provide reliable answers for the scientific question at hand. We showcase this methodology on a diverse set of aqueous systems with increasing degrees of complexity. The systems chosen here comprise bulk water with different ions in solution, water on a titanium dioxide surface, as well as water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems.

physics.chem-ph↗

arXiv2021

Benzene Radical Anion Microsolvated in Ammonia Clusters: Modelling the Transition from an Unbound Resonance to a Bound Species

The benzene radical anion, well-known in organic chemistry as the first intermediate in the Birch reduction of benzene in liquid ammonia, exhibits intriguing properties from the point of view of quantum chemistry. Notably, it has the character of a metastable shape resonance in the gas phase, while measurements in solution find it to be experimentally detectable and stable. In this light, our previous calculations performed in bulk liquid ammonia explicitly reveal that solvation leads to stabilization. Here, we focus on the transition of the benzene radical anion from an unstable gas-phase ion to a fully solvated bound species by explicit ionization calculations of the radical anion solvated in molecular clusters of increasing size. The computational cost of the largest systems is mitigated by combining density functional theory with auxiliary methods including effective fragment potentials or approximating the bulk by polarizable continuum models. Using this methodology, we obtain the cluster size dependence of the vertical binding energy of the benzene radical anion converging to the value of $-$2.3 eV at a modest computational cost.

physics.chem-ph↗

arXiv2020

Significance of nuclear quantum effects in hydrogen bonded molecular chains

In hydrogen bonded systems, nuclear quantum effects such as zero-point motion and tunneling can significantly affect their material properties through underlying physical and chemical processes. Presently, direct observation of the influence of nuclear quantum effects on the strength of hydrogen bonds with resulting structural and electronic implications remains elusive, leaving opportunities for deeper understanding to harness their fascinating properties. We studied hydrogen-bonded one-dimensional quinonediimine molecular networks which may adopt two isomeric electronic configurations via proton transfer. Herein, we demonstrate that concerted proton transfer promotes a delocalization of π-electrons along the molecular chain, which enhances the cohesive energy between molecular units, increasing the mechanical stability of the chain and giving rise to new electronic in-gap states localized at the ends. These findings demonstrate the identification of a new class of isomeric hydrogen bonded molecular systems where nuclear quantum effects play a dominant role in establishing their chemical and physical properties. We anticipate that this work will open new research directions towards the control of mechanical and electronic properties of low-dimensional molecular materials via concerted proton tunneling.

cond-mat.mes-hall↗

arXiv2020

Committee neural network potentials control generalization errors and enable active learning

It is well known in the field of machine learning that committee models improve accuracy, provide generalization error estimates, and enable active learning strategies. In this work, we adapt these concepts to interatomic potentials based on artificial neural networks. Instead of a single model, multiple models that share the same atomic environment descriptors yield an average that outperforms its individual members as well as a measure of the generalization error in the form of the committee disagreement. We not only use this disagreement to identify the most relevant configurations to build up the model's training set in an active learning procedure, but also monitor and bias it during simulations to control the generalization error. This facilitates the adaptive development of committee neural network potentials and their training sets, while keeping the number of ab initio calculations to a minimum. To illustrate the benefits of this methodology, we apply it to the development of a committee model for water in the condensed phase. Starting from a single reference ab initio simulation, we use active learning to expand into new state points and to describe the quantum nature of the nuclei. The final model, trained on 814 reference calculations, yields excellent results under a range of conditions, from liquid water at ambient and elevated temperatures and pressures to different phases of ice, and the air-water interface - all including nuclear quantum effects. This approach to committee models will enable the systematic development of robust machine learning models for a broad range of systems.

physics.chem-ph↗

arXiv2020

Benzene radical anion in the context of the Birch reduction: when solvation is the key

The benzene radical anion is an important intermediate in the Birch reduction of benzene by solvated electrons in liquid ammonia. Beyond organic chemistry, it is an intriguing subject of spectroscopic and theoretical studies due to its rich structural and dynamical behavior. In the gas phase, the species appears as a metastable shape resonance, while in the condensed phase it remains stable. Here, we approach the system by ab initio molecular dynamics in liquid ammonia and demonstrate that the inclusion of solvent is crucial and indeed leads to stability. Beyond the mere existence of the radical anion species, our simulations explore its condensed-phase behavior at the molecular level and offer new insights into its properties. These include the dynamic Jahn-Teller distortions, vibrational spectra in liquid ammonia, and the structure of the solvent shell, including the motif of a $π$-hydrogen bond between ammonia molecules and the aromatic ring.

physics.chem-ph↗

arXiv2018

Unravelling the influence of quantum proton delocalization on electronic charge transfer through the hydrogen bond

Upon hydrogen bond formation, electronic charge density is transferred between the donor and acceptor, impacting processes ranging from hydration to spectroscopy. Here we use ab initio path integral simulations to elucidate the role of nuclear quantum effects in determining the charge transfer in a range of hydrogen bonded species in the gas and liquid phase. We show that the quantization of the nuclei gives rise to large changes in the magnitude of the charge transfer as well as its temperature dependence. We then explain how a single geometric parameter determines the charge transfer through the hydrogen bond. These results thus demonstrate that nuclear quantum effects are vital for the accurate description of charge transfer and offer a physically transparent way to understand how hydrogen bonding gives rise to it.

physics.chem-ph↗

arXiv2018

i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations

Progress in the atomic-scale modelling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the Born-Oppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives.

physics.chem-ph↗

arXiv2017

The Interplay of Structure and Dynamics in the Raman Spectrum of Liquid Water over the Full Frequency and Temperature Range

While many vibrational Raman spectroscopy studies of liquid water have investigated the temperature dependence of the high-frequency O-H stretching region, few have analyzed the changes in the Raman spectrum as a function of temperature over the entire spectral range. Here, we obtain the Raman spectra of water from its melting to boiling point, both experimentally and from simulations using an ab initio-trained machine learning potential. We use these to assign the Raman bands and show that the entire spectrum can be well described as a combination of two temperature-independent spectra. We then assess which spectral regions exhibit strong dependence on the local tetrahedral order in the liquid. Further, this work demonstrates that changes in this structural parameter can be used to elucidate the temperature dependence of the Raman spectrum of liquid water and provides a guide to the Raman features that signal water ordering in more complex aqueous systems.

physics.chem-ph↗

arXiv2017

Decoding the spectroscopic features and timescales of aqueous proton defects

Acid solutions exhibit a variety of complex structural and dynamical features arising from the presence of multiple interacting reactive proton defects and counterions. However, disentangling the transient structural motifs of proton defects in the water hydrogen bond network and the mechanisms for their interconversion remains a formidable challenge. Here, we use simulations treating the quantum nature of both the electrons and nuclei to show how the experimentally observed spectroscopic features and relaxation timescales can be elucidated using a physically transparent coordinate that encodes the overall asymmetry of the solvation environment of the proton defect. We demonstrate that this coordinate can be used both to discriminate the extremities of the features observed in the linear vibrational spectrum and to explain the molecular motions that give rise to the interconversion timescales observed in recent nonlinear experiments. This analysis provides a unified condensed-phase picture of proton structure and dynamics that, at its extrema, encompasses proton sharing and spectroscopic features resembling the limiting Eigen [H$_{3}$O(H$_{2}$O)$_{3}$]$^{+}$ and Zundel [H(H$_{2}$O)$_{2}$]$^{+}$ gas-phase structures, while also describing the rich variety of interconverting environments in the liquid phase.

physics.chem-ph↗

arXiv2017

Quantum dynamics and spectroscopy of ab initio liquid water: the interplay of nuclear and electronic quantum effects

Understanding the reactivity and spectroscopy of aqueous solutions at the atomistic level is crucial for the elucidation and design of chemical processes. However, the simulation of these systems requires addressing the formidable challenges of treating the quantum nature of both the electrons and nuclei. Exploiting our recently developed methods that provide acceleration by up to two orders of magnitude, we combine path integral simulations with on-the-fly evaluation of the electronic structure at the hybrid density functional theory level to capture the interplay between nuclear quantum effects and the electronic surface. Here we show that this combination provides accurate structure and dynamics, including the full infra-red and Raman spectra of liquid water. This allows us to demonstrate and explain the failings of lower-level density functionals for dynamics and vibrational spectroscopy when the nuclei are treated quantum mechanically. These insights thus provide a foundation for the reliable investigation of spectroscopy and reactivity in aqueous environments.

physics.chem-ph↗

arXiv2016

Competing quantum effects in the free energy profiles and diffusion rates of hydrogen and deuterium molecules through clathrate hydrates

Clathrate hydrates hold considerable promise as safe and economical materials for hydrogen storage. Here we present a quantum mechanical study of H$_2$ and D$_2$ diffusion through a hexagonal face shared by two large cages of clathrate hydrates over a wide range of temperatures. Path integral molecular dynamics simulations are used to compute the free-energy profiles for the diffusion of H$_2$ and D$_2$ as a function of temperature. Ring polymer molecular dynamics rate theory, incorporating both exact quantum statistics and approximate quantum dynamical effects, is utilized in the calculations of the H$_2$ and D$_2$ diffusion rates in a broad temperature interval. We find that the shape of the quantum free-energy profiles and their height relative to the classical free energy barriers at a given temperature, as well as the rate of diffusion, are profoundly affected by competing quantum effects: above 25 K, zero-point energy (ZPE) perpendicular to the reaction path for diffusion between cavities decreases the quantum rate compared to the classical rate, whereas at lower temperatures tunneling outcompetes the ZPE and as result the quantum rate is greater than the classical rate.

physics.chem-ph↗