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Subject: search.filters.subject.530Institute: search.filters.UBSInstitut.Stuttgarter Zentrum für Simulationswissenschaften (SC SimTech)Institute: search.filters.UBSInstitut.Institut für Computerphysik

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    Water structuring induces nonuniversal hydration repulsion between polar surfaces : quantitative comparison between molecular simulations, theory, and experiments
    (2024) Schlaich, Alexander; Daldrop, Jan O.; Kowalik, Bartosz; Kanduč, Matej; Schneck, Emanuel; Netz, Roland R.
    Polar surfaces in water typically repel each other at close separations, even if they are charge-neutral. This so-called hydration repulsion balances the van der Waals attraction and gives rise to a stable nanometric water layer between the polar surfaces. The resulting hydration water layer is crucial for the properties of concentrated suspensions of lipid membranes and hydrophilic particles in biology and technology, but its origin is unclear. It has been suggested that surface-induced molecular water structuring is responsible for the hydration repulsion, but a quantitative proof of this water-structuring hypothesis is missing. To gain an understanding of the mechanism causing hydration repulsion, we perform molecular simulations of different planar polar surfaces in water. Our simulated hydration forces between phospholipid bilayers agree perfectly with experiments, validating the simulation model and methods. For the comparison with theory, it is important to split the simulated total surface interaction force into a direct contribution from surface-surface molecular interactions and an indirect water-mediated contribution. We find the indirect hydration force and the structural water-ordering profiles from the simulations to be in perfect agreement with the predictions from theoretical models that account for the surface-induced water ordering, which strongly supports the water-structuring hypothesis for the hydration force. However, the comparison between the simulations for polar surfaces with different headgroup architectures reveals significantly different decay lengths of the indirect water-mediated hydration-force, which for laterally homogeneous water structuring would imply different bulk-water properties. We conclude that laterally inhomogeneous water ordering, induced by laterally inhomogeneous surface structures, shapes the hydration repulsion between polar surfaces in a decisive manner. Thus, the indirect water-mediated part of the hydration repulsion is caused by surface-induced water structuring but is surface-specific and thus nonuniversal.
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    Consistent modeling of electrostatic interactions in confined electrode systems : thermodynamic behavior and macroscopic properties from atomistic simulations
    (2026) Stärk, Philipp; Holm, Christian (Prof. Dr.)
    The interface between metals and fluids underpins a wide range of technologies, including batteries, supercapacitors, sensors, and electrocatalytic devices such as fuel cells. Despite its importance, accurately modeling these systems remains challenging. On the atomic scale, the interactions governing metal–fluid interfaces are inherently quantum-mechanical, while many technologically relevant observables - such as capacitance - are collective, statistical properties emerging from large numbers of atoms. Bridging these scales is therefore a central problem in computational physics. This thesis develops and applies methods to address this multi-scale modeling challenge. We begin by establishing how statistical mechanics can systematically connect microscopic observables from atomistic simulations to pore-scale properties accessible in experiments. Using this framework, we examine recent measurements of the dielectric response of water confined within nanometer-sized pores. Our analysis shows that the reported deviations from bulk water behavior can be attributed not to intrinsic changes in the dielectric properties of water, but rather to ambiguities in defining pore size in experimental setups. In addition, this work advances the computation of dielectric response in molecular simulations. By carefully re-deriving the underlying equations and by analyzing and clarifying the role of electrostatic boundary conditions, we resolve several long-standing inconsistencies in the literature. To further develop the scientific understanding of how fluid-electrode interactions modify average material properties inside porous systems, we extend this framework to investigate dielectric response in the vicinity of conductive interfaces. To this end, we first construct an electrode model - based on explicit quantum-mechanical calculations within the density-functional-theory formalism - that accurately captures the electrostatic interactions near metals held at constant potential. This procedure allows us to use the computationally efficient method of empirical force fields needed to achieve sufficient statistics. Using extensions of the dielectric-response formalism developed in the previous chapter, we build a model capable of predicting capacitance at arbitrary pore sizes from a single atomistic simulation. A third scientific challenge addressed in this thesis concerns the thermodynamically accurate description of adsorption of charged species under confinement, with particular emphasis on conductive environments. To this end, we develop an extension of the Wang-Landau sampling method that enables accurate calculation of the grand potential for arbitrary mixtures. This thermodynamic potential - relevant for describing pore filling in systems in contact with an external reservoir - can then be used to predict the average occupancy of a pore for given thermodynamic control variables. We apply this method to investigate the critical liquid-vapor transition of a simple model fluid, the modified restricted primitive model. Beyond the accurate characterization of the critical point in bulk systems, we extend our study to porous systems and find that confinement shifts the critical point to lower temperatures. Moreover, we show that the presence of conducting boundaries substantially alters the chemical potential at coexistence, underscoring the influence of conductivity on critical behavior. Remarkably, we also identify a pore-size threshold below which no critical behavior is observed - a result that contrasts with previous mean-field theories and models that neglect electrostatic interactions. Finally, as an outlook, we describe our work on developing machine-learned interatomic potentials capable of accurately predicting the response of atomistic systems to externally applied electrostatic fields. While machine-learned potentials have become indispensable for modeling materials with near ab-initio accuracy at greatly reduced computational cost, most standard implementations do not explicitly account for field-response properties. Because this capability is central to the problems addressed in this thesis, extending machine-learned interatomic potentials to include field-response forms the basis for further investigations in the spirit of the previous chapters. We systematically explore strategies for incorporating predictions of atomic polar tensors (also known as Born effective charges), which quantify the response of quantum systems to applied fields, into machine learning based interatomic models, with particular attention to long-range formalisms. Our results show that accurate modeling of electrostatic response is achieved most effectively when atomic polar tensors are predicted independently and we offer interpretable reasons for why this is the case. In summary, this thesis advances the computational modeling of metal-fluid interfaces by establishing rigorous statistical-mechanical links between atomistic simulations and experimentally accessible pore-scale observables. We clarify the formalism for calculating the dielectric behavior of confined water, and resolve conceptual inconsistencies found in the literature. Building on this foundation, we develop a constant-potential electrode model rooted in quantum-mechanical calculations, enabling efficient and accurate predictions of capacitance across pore sizes. We further introduce a generalized Wang–Landau approach for charged mixtures in confinement, which reveals how pore geometry and conductivity shape critical phenomena and adsorption thermodynamics and can be used for further study of adsorption thermodynamics. Finally, we outline a machine-learning framework for interatomic potentials that incorporates atomic polar tensors, providing a promising route toward accurate long-range electrostatic response modeling.
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    Simulating stochastic processes with variational quantum circuits
    (2022) Fink, Daniel
    Simulating future outcomes based on past observations is a key task in predictive modeling and has found application in many areas ranging from neuroscience to the modeling of financial markets. The classical provably optimal models for stationary stochastic processes are so-called ϵ-machines, which have the structure of a unifilar hidden Markov model and offer a minimal set of internal states. However, these models are not optimal in the quantum setting, i.e., when the models have access to quantum devices. The methods proposed so far for quantum predictive models rely either on the knowledge of an ϵ-machine, or on learning a classical representation thereof, which is memory inefficient since it requires exponentially many resources in the Markov order. Meanwhile, variational quantum algorithms (VQAs) are a promising approach for using near-term quantum devices to tackle problems arising from many different areas in science and technology. Within this work, we propose a VQA for learning quantum predictive models directly from data on a quantum computer. The learning algorithm is inspired by recent developments in the area of implicit generative modeling, where a kernel-based two-sample-test, called maximum mean discrepancy (MMD), is used as a cost function. A major challenge of learning predictive models is to ensure that arbitrarily many time steps can be simulated accurately. For this purpose, we propose a quantum post-processing step that yields a regularization term for the cost function and penalizes models with a large set of internal states. As a proof of concept, we apply the algorithm to a stationary stochastic process and show that the regularization leads to a small set of internal states and a constantly good simulation performance over multiple future time steps, measured in the Kullback-Leibler divergence and the total variation distance.
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    Renormalized charge and dielectric effects in colloidal interactions : a numerical solution of the nonlinear Poisson-Boltzmann equation for unknown boundary conditions
    (2023) Schlaich, Alexander; Tyagi, Sandeep; Kesselheim, Stefan; Sega, Marcello; Holm, Christian
    The Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, introduced more than 70 years ago, is a hallmark of colloidal particle modeling. For highly charged particles in the dilute regime, it is often supplemented by Alexander’s prescription (Alexander et al. in J Chem Phys 80:5776, 1984) for using a renormalized charge. Here, we solve the problem of the interaction between two charged colloids at finite ionic strength, including dielectric mismatch effects, using an efficient numerical scheme to solve the nonlinear Poisson-Boltzmann (NPB) equation with unknown boundary conditions. Our results perfectly match the analytical predictions for the renormalized charge by Trizac and coworkers (Aubouy et al. in J Phys A 36:5835, 2003). Moreover, they allow us to reinterpret previous molecular dynamics (MD) simulation results by Kreer et al. (Phys Rev E 74:021401, 2006), rendering them now in agreement with the expected behavior. We furthermore find that the influence of polarization becomes important only when the Debye layers overlap significantly.