arXiv subjects

Stephen J. Cox

Publications and source records attributed to Stephen J. Cox.

At least 19 recordsLinked to original sources

arXiv2026

A unified machine-learning framework for ab initio multiscale modeling of liquids

Understanding and predicting the behavior of liquid matter across length scales, using only the microscopic interactions encoded in the Schrödinger equation, remains a central challenge in the physical sciences. Achieving this goal requires not only an accurate and efficient description of intermolecular forces but also a consistent framework that bridges the micro-, meso-, and macroscales. Here, by combining machine-learned interatomic potentials (MLIPs) with neural classical density functional theory (neural cDFT), we present such a framework. The underlying idea is simple: MLIPs trained on quantum-mechanical energies and forces are used to generate inhomogeneous microscopic density profiles, which in turn serve as the training data for neural cDFT. The resulting ab initio neural cDFT is not only significantly more computationally efficient than molecular simulations, but also provides a conceptually transparent route to the thermodynamics of both homogeneous and inhomogeneous systems. We demonstrate the approach for both water and carbon dioxide using several exchange-correlation functionals. Beyond accurately reproducing bulk equations of state and liquid-vapor phase diagrams, ab initio neural cDFT predicts, from first principles, how confinement modifies liquid-vapor coexistence in water. It also captures complex behavior in supercritical carbon dioxide such as the Fisher-Widom and Widom lines. Ab initio neural cDFT establishes a general first-principles route to multiscale modeling of fluids within a single unified conceptual framework.

physics.chem-ph↗

arXiv2026

Molecular interpretability of the bulk electrochemical impedance of concentrated electrolytes

Electrochemical impedance spectroscopy (EIS) is a widely used technique to understand time-dependent response and relaxation under applied voltage. While these spectra contain a wealth of information, major gaps in our understanding can hinder our ability to interpret EIS spectra in terms of microscopic chemical mechanisms. We propose an alternative approach to common empirical fitting procedures for describing the contribution of the bulk electrolyte to the EIS spectrum. This new approach is rooted in determining the moments of the frequency-dependent conductivity, with molecular interpretability provided by a generalized Langevin equation description of an effective single particle dynamics; the `itinerant oscillator' (IO) model. In contrast to a Debye--Falkenhagen description, the IO model makes no assumptions regarding the concentration of the electrolyte, a fact we demonstrate by analysing molecular dynamics simulations of a room-temperature ionic liquid. By analysing the memory function from simulation within the framework provided by the IO model, we reveal the importance of capturing the separation of timescales within the memory function for describing the temperature dependent $β$-relaxation process. We go on to show how our impedance model directly reports on this distribution of timescales while retaining the simplicity of commonly employed workflows.

physics.chem-ph↗

arXiv2026

The roles of bulk and surface thermodynamics in the selective adsorption of a confined azeotropic mixture

Fluid mixtures that exhibit an azeotrope cannot be purified by simple bulk distillation. Consequently, there is strong motivation to understand the behavior of azeotropic mixtures under confinement. We address this problem using an ML-enhanced classical density functional theory (cDFT) applied to a binary Lennard-Jones mixture that exhibits azeotropic phase behavior. As proof-of-principle of a "train once, learn many" strategy, our approach combines a neural functional trained on a single-component repulsive reference system with a mean-field treatment of attractive interactions, inspired by the connection between cDFT and local molecular field theory. The theory faithfully describes capillary condensation and results from grand canonical Monte Carlo simulations. Moreover, by taking advantage of a known accurate equation of state, the "neural LMFT" we present well-describes bulk thermodynamics by construction. Exploiting the computational efficiency of neural LMFT, we systematically evaluate adsorption selectivity across a wide range of compositions, pressures, temperatures, and wall-fluid affinities. In cases where the wall-fluid interaction is the same for both species, we find that the pore becomes completely unselective at the bulk azeotropic composition. Strikingly, this unselective point persists far from liquid-vapor coexistence, including in the supercritical regime. Analysis of the bulk equation of state across a wide range of thermodynamic state points shows that the azeotropic composition coincides with equal partial molar volumes and an extremum in the isothermal compressibility. A thermodynamic analysis demonstrates that unselective adsorption corresponds to an aneotrope (a point of zero relative adsorption) and an extremum in the interfacial free energy. We also find that the two interfaces of the slit pore behave independently down to remarkably small slits.

cond-mat.stat-mech↗

arXiv2025

Dielectrocapillarity for exquisite control of fluids

Spatially varying electric fields are prevalent throughout nature, such as in nanoporous materials and biological membranes, and technology, e.g, patterned electrodes and van der Waals heterostructures. While uniform fields cause free ions to migrate, for polar fluids they simply reorient the constituent molecules. In contrast, electric field gradients (EFGs) induce a dielectrophoretic force, offering fine control of polar fluids even in the absence of free charges. Despite their vast potential for optimizing fluid behavior under confinement, such as in nanoporous electrodes, nanofluidic devices, and chemical separation materials. EFGs remain largely unexplored at the microscopic level due to the absence of a rigorous first-principles theory of electrostriction. By integrating state-of-the-art advances in liquid state theory and deep learning, we reveal how EFGs modulate fluid structure and capillarity. We demonstrate that dielectrophoretic coupling enables tunable control over the liquid-gas phase transition, capillary condensation, and fluid uptake into porous media. Our findings establish "dielectrocapillarity" -- the use of EFGs to manipulate confined fluids -- as a powerful mechanism for controlling volumetric capacity in nanopores, holding immense potential for energy storage, selective gas separation, and tunable hysteresis in neuromorphic nanofluidics. Furthermore, by linking nanoscale dielectrocapillarity to macroscopic dielectrowetting, we establish a foundation for field-controlled wetting and adsorption phenomena of polar fluids across length scales.

cond-mat.soft↗

arXiv2025

Nanoconfined superionic water is a molecular superionic

Superionic ice, where water molecules dissociate into a lattice of oxygen ions and a rapidly diffusing 'gas' of protons, represents an exotic state of matter with broad implications for planetary interiors and energy applications. Recently, a nanoconfined superionic state of water has been predicted which, in sharp contrast to bulk ice, is comprised of intact water molecules. Here, we apply machine learning and electronic structure simulations to establish how nanoconfined water can be both molecular and superionic. We also explore what insights this material offers for superionic materials and behavior more generally. Similar to bulk ice and other superionic materials, nanoconfined water conducts via concerted chain-like proton migrations which cause the rapid propagation of defects. However, unlike other molecular phases of water, its exceptional conductivity arises from the activation of the Grotthuss mechanism by (i) low barriers to proton transfer and (ii) a flexible hydrogen-bonded network. We propose that these are two key characteristics of fast ionic conduction in molecular superionics. The insights obtained here establish design principles for the discovery of other molecular superionic materials, with potential applications in energy storage and beyond.

physics.chem-ph↗

arXiv2025

A first principles approach to electromechanics in liquids

Electromechanics in fluids describes the response of the number density to electric fields, and thus provides a powerful means by which to control the behavior of liquids. While continuum approaches have proven successful in describing electromechanical phenomena in macroscopic bodies, their use is questionable when relevant length scales become comparable to a system's natural correlation lengths, as commonly occurs in, e.g., biological systems, nanopores, and microfluidics. Here, we present a first principles theory for electromechanical phenomena in fluids. Our approach is based on the recently proposed hyperdensity functional theory [Sammüller et al, Phys. Rev. Lett. 133, 098201 (2024)] in which we treat the charge density as an observable of the system, with the intrinsic Helmholtz free energy functional dependent upon both density and electrostatic potential. Expressions for the coupling between number and charge densities emerge naturally in this formalism, avoiding the need to construct density-dependent and spatially-varying material parameters such as the dielectric constant. Furthermore, we make our theory practical by deriving a connection between hyperdensity functional theory and local molecular field theory, which facilitates machine learning explicit representations for the free energy functionals of systems with short-ranged electrostatic interactions, with long-ranged effects accounted for in a well-controlled mean field fashion.

cond-mat.soft↗

arXiv2025

Learning classical density functionals for ionic fluids

Accurate and efficient theoretical techniques for describing ionic fluids are highly desirable for many applications across the physical, biological and materials sciences. With a rigorous statistical mechanical foundation, classical density functional theory (cDFT) is an appealing approach, but the competition between strong Coulombic interactions and steric repulsion limits the accuracy of current approximate functionals. Here, we extend a recently presented machine learning (ML) approach [Sammüller et al., Proc. Natl. Acad. Sci. USA, 120, e2312484120 (2023)] designed for systems with short-ranged interactions to ionic fluids. By adopting ideas from local molecular field theory, the framework we present amounts to using neural networks to learn the local relationship between the one-body direct correlation functions and inhomogeneous density profiles for a "mimic'' short-ranged system, with effects of long-ranged interactions accounted for in a mean-field, yet well-controlled, manner. By comparing to results from molecular simulations, we show that our approach accurately describes the structure and thermodynamics of prototypical models for electrolyte solutions and ionic liquids, including size-asymmetric and multivalent systems. The framework we present acts as an important step toward extending ML approaches for cDFT to systems with accurate interatomic potentials.

cond-mat.stat-mech↗

arXiv2025

Momentum tunnelling between nanoscale liquid flows

The world of nanoscales in fluidics is the frontier where the continuum of fluid mechanics meets the atomic, and even quantum, nature of matter. While water dynamics remains largely classical under extreme confinement, several experiments have recently reported coupling between water transport and the electronic degrees of freedom of the confining materials. This avenue prompts us to reconsider nanoscale hydrodynamic flows under the perspective of interacting excitations, akin to condensed matter frameworks. Here we show, using a combination of many-body theory and molecular simulations, that the flow of a liquid can induce the flow of another liquid behind a separating wall, at odds with the prediction of continuum hydrodynamics. We further show that the range of this 'flow tunnelling' can be tuned through the solid's electronic excitations, with a maximum occurring when these are at resonance with the liquid's charge density fluctuations. Flow tunnelling is expected to play a role in global transport across nanoscale fluidic networks, such as lamellar graphene oxide or MXene membranes. It further suggests exploiting the electronic properties of the confining walls for manipulating liquids via their dielectric spectra, beyond the nature and characteristics of individual molecules.

cond-mat.mes-hall↗

arXiv2024

Revisiting the Green-Kubo relation for friction in nanofluidics

A central aim of statistical mechanics is to establish connections between a system's microscopic fluctuations and its macroscopic response to a perturbation. For non-equilibrium transport properties, this amounts to establishing Green-Kubo (GK) relationships. In hydrodynamics, relating such GK expressions for liquid-solid friction to macroscopic slip boundary conditions has remained a long-standing problem due to two challenges: (i) The GK running integral of the force autocorrelation function decays to zero rather than reaching a well-defined plateau value; and (ii) debates persist on whether such a transport coefficient measures an intrinsic interfacial friction or an effective friction in the system. Inspired by ideas from the coarse-graining community, we derive a GK relation for liquid-solid friction where the force autocorrelation is sampled with a constraint of momentum conservation in the liquid. Our expression does not suffer from the "plateau problem" and unambiguously measures an effective friction coefficient, in an analogous manner to Stokes' law. We further establish a link between the derived friction coefficient and the hydrodynamic slip length, enabling a straightforward assessment of continuum hydrodynamics across length scales. We find that continuum hydrodynamics describes the simulation results quantitatively for confinement length scales all the way down to 1 nm. Our approach amounts to a straightforward modification to the present standard method of quantifying interfacial friction from molecular simulations, making possible a sensible comparison between surfaces of vastly different slippage.

cond-mat.stat-mech↗

arXiv2024

Symmetry Breaking in the Superionic Phase of Silver-Iodide

In the superionic phase of silver iodide, we observe a distorted tetragonal structure characterized by symmetry breaking in the cation distribution. This phase competes with the well known bcc phase, with a symmetric cation distribution at an energetic cost of only a few meV/atom. The small energy difference suggests that these competing structures may both be thermally accessible near the superionic transition temperature. We also find that the distribution of silver ions depends on the low-temperature parent polymorph, with memory persisting in the superionic phase on the nanosecond time scales accessible in our simulations. Furthermore, simulations on the order 100ns reveal that even at temperatures where the bcc phase is stable, significant fluctuations toward the tetragonal lattice structure remain. Our results are consistent with many "anomalous" experimental observations and offer a molecular mechanism for the "memory effect" in silver iodide.

cond-mat.mtrl-sci↗

arXiv2024

A classical density functional theory for solvation across length scales

A central aim of multiscale modeling is to use results from the Schrödinger Equation to predict phenomenology on length scales that far exceed those of typical molecular correlations. In this work, we present a new approach rooted in classical density functional theory (cDFT) that allows us to accurately describe the solvation of apolar solutes across length scales. Our approach builds on the Lum-Chandler-Weeks (LCW) theory of hydrophobicity [K. Lum et al., J. Phys. Chem. B 103, 4570 (1999)] by constructing a free energy functional that uses a slowly-varying component of the density field as a reference. From a practical viewpoint, the theory we present is numerically simpler and generalizes to solutes with soft-core repulsion more easily than LCW theory. Furthermore, by assessing the local compressibility and its critical scaling behavior, we demonstrate that our LCW-style cDFT approach contains the physics of critical drying, which has been emphasized as an essential aspect of hydrophobicity by recent theories. As our approach is parameterized on the two-body direct correlation function of the uniform fluid and the liquid-vapor surface tension, it straightforwardly captures the temperature dependence of solvation. Moreover, we use our theory to describe solvation at a first-principles level, on length scales that vastly exceed what is accessible to molecular simulations.

cond-mat.stat-mech↗

arXiv2023

The limit of macroscopic homogeneous ice nucleation at the nanoscale

Nucleation in small volumes of water has garnered renewed interest due to the relevance of pore condensation and freezing under conditions of low partial pressures of water, such as in the upper troposphere. Molecular simulations can in principle provide insight on this process at the molecular scale that is challenging to achieve experimentally. However, there are discrepancies in the literature as to whether the rate in confined systems is enhanced or suppressed relative to bulk water at the same temperature and pressure. In this study, we investigate the extent to which the size of the critical nucleus and the rate at which it grows in thin films of water are affected by the thickness of the film. Our results suggest that nucleation remains bulk-like in films that are barely large enough accommodate a critical nucleus. This conclusion seems robust to the presence of physical confining boundaries. We also discuss the difficulties in unambiguously determining homogeneous nucleation rates in nanoscale systems, owing to the challenges in defining the volume. Our results suggest any impact on a film's thickness on the rate is largely inconsequential for present day experiments.

physics.chem-ph↗

arXiv2023

Remembering the work of Phillip L. Geissler: A coda to his scientific trajectory

Phillip L. Geissler made important contributions to the statistical mechanics of biological polymers, heterogeneous materials, and chemical dynamics in aqueous environments. He devised analytical and computational methods that revealed the underlying organization of complex systems at the frontiers of biology, chemistry, and materials science. In this retrospective, we celebrate his work at these frontiers.

cond-mat.stat-mech↗

arXiv2023

Classical quantum friction at water-carbon interfaces

Friction at water-carbon interfaces remains a major puzzle with theories and simulations unable to explain experimental trends in nanoscale waterflow. A recent theoretical framework -- quantum friction (QF)-- proposes to resolve these experimental observations by considering nonadiabatic coupling between dielectric fluctuations in water and graphitic surfaces. Here, using a classical model that enables fine-tuning of the solid's dielectric spectrum, we provide evidence from simulations in general support of QF. In particular, as features in the solid's dielectric spectrum begin to overlap with water's librational and Debye modes, we find an increase in friction in line with that proposed by QF. At the microscopic level, we find that this contribution to friction manifests more distinctly in the dynamics of the solid's charge density than that of water. Our findings suggest that experimental signatures of QF may be more pronounced in the solid's response rather than liquid water's.

cond-mat.soft↗

arXiv2022

Crumbling Crystals: On the Dissolution Mechanism of NaCl in Water

Life on Earth depends upon the dissolution of ionic salts in water, particularly NaCl. However, an atomistic scale understanding of the process remains elusive. Simulations lend themselves conveniently to studying dissolution since they provide the spatio-temporal resolution that can be difficult to obtain experimentally. Nevertheless, the complexity of various inter- and intra-molecular interactions require careful treatment and long time scale simulations, both of which are typically hindered by computational expense. Here, we use advances in machine learning potential methodology to resolve for the first time at an ab initio level of theory the dissolution mechanism of NaCl in water. The picture that emerges is that of a steady ion-wise unwrapping of the crystal preceding its rapid disintegration, reminiscent of crumbling. The onset of crumbling can be explained by a strong increase in the ratio of the surface to volume of the crystal. Overall, dissolution is comprised of a series of highly dynamical microscopic sub-processes, resulting in an inherently stochastic mechanism. These atomistic level insights now pave the way for a general understanding of dissolution mechanisms in other crystals, and the methodology is primed for more complex systems of recent interest such as water/salt interfaces under flow and salt crystals under confinement.

physics.chem-ph↗

arXiv2022

A theory for the stabilization of polar crystal surfaces by a liquid environment

Polar crystal surfaces play an important role in the functionality of many materials, and have been studied extensively over many decades. In this article, a theoretical framework is presented that extends existing theories by placing the surrounding solution environment on an equal footing with the crystal itself; this is advantageous, e.g., when considering processes such as crystal growth from solution. By considering the polar crystal as a stack of parallel plate capacitors immersed in a solution environment, the equilibrium adsorbed surface charge density is derived by minimizing the free energy of the system. In analogy to the well-known diverging surface energy of a polar crystal surface at zero temperature, for a crystal in solution it is shown that the "polar catastrophe" manifests as a diverging free energy cost to perturb the system from equilibrium. Going further than existing theories, the present formulation predicts that fluctuations in the adsorbed surface charge density become increasingly suppressed with increasing crystal thickness. We also show how, in the slab geometry often employed in both theoretical and computational studies of interfaces, an electric displacement field emerges as an electrostatic boundary condition, the origins of which are rooted in the slab geometry itself, rather than the use of periodic boundary conditions. This aspect of the work provides a firmer theoretical basis for the recent observation that standard "slab corrections" fail to correctly describe, even qualitatively, polar crystal surfaces in solution.

cond-mat.stat-mech↗

arXiv2022

Dielectric response of thin water films: A thermodynamic perspective

The surface of a polar liquid presents a special environment for the solvation and organization of charged solutes, which differ from bulk behaviors in important ways. These differences have motivated many attempts to understand electrostatic response at aqueous interfaces in terms of a spatially varying dielectric permittivity, typically concluding that the dielectric constant of interfacial water is significantly lower than in the bulk liquid. Such analyses, however, are complicated by the potentially nonlocal nature of dielectric response over the short length scales of interfacial heterogeneity. Here we circumvent this problem for thin water films by adopting a thermodynamic approach. Using molecular simulations, we calculate the solvent's contribution to the reversible work of charging a parallel plate capacitor. We find good agreement with a simple dielectric continuum model that assumes bulk dielectric permittivity all the way up to the liquid's boundary, even for very thin ($\sim 1$ nm) films. This comparison requires careful attention to the placement of dielectric boundaries between liquid and vapor, which also resolves apparent discrepancies with dielectric imaging experiments.

cond-mat.soft↗

arXiv2022

Can molecular simulations reliably compare homogeneous and heterogeneous ice nucleation?

In principle, the answer to the posed titular question is undoubtedly 'yes.' But in practice, requisite reference data for homogeneous systems have been obtained with a treatment of intermolecular interactions that is different from that typically employed for heterogeneous systems. In this article, we assess the impact of the choice of truncation scheme when comparing water in homogeneous and inhomogeneous environments. Specifically, we use explicit free energy calculations and a simple mean field analysis to demonstrate that using the 'cut-and-shift' version of the Lennard-Jones potential (common to most simple point charge models of water) results in a systematic increase in the melting temperature of ice I$_{\rm h}$. In addition, by drawing an analogy between a change in cutoff and a change in pressure, we use existing literature data for homogeneous ice nucleation at negative pressures to suggest that enhancements due to heterogeneous nucleation may have been overestimated by several orders of magnitude.

cond-mat.stat-mech↗