arXiv subjects

Xavier R. Advincula

Publications and source records attributed to Xavier R. Advincula.

12 recordsLinked to original sources

arXiv2026

Strain-Dependent Wetting of Graphene

Understanding how water wets graphene is critical for predicting and controlling its behaviour in nanofluidic, sensing, and energy applications. A key measure of wetting is the contact angle made by a liquid droplet against the surface, yet experimental measurements for graphene span a wide range, with no consensus for free-standing graphene. Here, we use a machine learning potential with ab initio accuracy to provide an atomistic first-principles prediction for this unsolved problem, finding a weakly hydrophilic contact angle of $72.1 \pm 1.5 °$. More importantly, we unveil that graphene's wetting properties are highly sensitive to mechanical strain: tensile strain makes graphene significantly less hydrophilic, while compressive strain induces coherent ripples around the droplet, resulting in pronounced anisotropic wetting and contact angle hysteresis. We show that there is a strong coupling between the three-phase contact line and the intrinsic thermal ripples of free-standing graphene, which contributes to this strain sensitivity. Our results demonstrate that the wettability of 2D membranes are governed not only by their chemistry but also by their dynamic morphology, introducing a new class of wetting behaviour unique to atomically thin materials that offers an additional explanation for variability in experimental measurements. These findings reveal that mechanical strain may be a practical route to controlling wetting in 2D nanomaterials-based technologies, with promising consequences for nanofluidic and nano-filtration applications.

cond-mat.mes-hall↗

arXiv2025

When Is Nanoconfined Water Different From Interfacial Water?

Water behaves very differently at surfaces and under extreme confinement, but the boundary between these two regimes has remained unclear. Despite evidence that interfacial effects persist under sub-nanometre confinement, the molecular-scale behaviour and its evolution with slit width remain unclear. Here, we use machine-learning molecular dynamics with first-principles accuracy to probe water at graphene surfaces across slit widths ranging from the open-interface limit to angstrom-scale confinement. We find that water undergoes a sharp structural transition: when three or more water layers fit between the walls, the structure of the graphene-water interface is effectively indistinguishable from that in an open system, with density layering, hydrogen bonding, and orientational ordering retaining interfacial character. Below this threshold, however, angstrom-scale confinement strongly reorganises the liquid, producing enhanced ordering, a restructured hydrogen-bond network, and modified orientational motifs. These results establish a molecular-level picture that clearly separates interfacial behaviour from genuine nanoconfinement and provide guidance for predicting and controlling the structure of water in nanoscale solid-liquid environments.

physics.chem-ph↗

arXiv2025

Breaking the Air-Water Paradigm: Ion Behavior at Hydrophobic Solid-Water Interfaces

Hydrophobic solid-water interfaces underpin processes in nanofluidics, electrochemistry, and energy technologies. Microscopic insights into these systems are often inferred from our understanding of the air-water interface, which is assumed to exhibit similar behavior. Here, we challenge this paradigm by combining heterodyne-detected vibrational sum-frequency generation spectroscopy with machine-learning molecular dynamics simulations at first-principles accuracy to investigate the graphene-NaCl(aq) interface as a prototypical hydrophobic solid-water system. Spectroscopic results suggest that ions have a minimal effect on the structure of the interfacial water, while simulations reveal that Na$^{+}$ and Cl$^{-}$ accumulate densely at the surface. Together, these findings reveal a new adsorption mechanism that departs from the established air-water interface paradigm, where interfacial ion adsorption is typically associated with, and often detected through, pronounced alteration of the interfacial water alignment and orientation. This difference arises because ions cannot penetrate the solid boundary and reside at a similar depth as the interfacial water molecules. As a consequence, large ion populations can be accommodated within the extended two-dimensional hydrogen-bond network at the interface, causing only minor local distortions but significant changes to its longer-range connectivity. These results reveal a distinct mechanism of electrolyte organization at aqueous-carbon interfaces, relevant to energy applications, where performance is highly sensitive to the local organization of interfacial water.

physics.chem-ph↗

arXiv2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early ML force fields have largely been limited by: (i) the substantial computational and human effort of developing and validating potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model - and its qualitative and at times quantitative accuracy - on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users get reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step towards democratising the revolution in atomic-scale modeling that has been brought about by ML force fields.

physics.chem-ph↗

arXiv2025

Clay Edges Are Dynamic Proton-conducting Networks Modulated by Structure and pH

Montmorillonite, a ubiquitous clay mineral, plays a vital role in geochemical and environmental processes due to its chemically complex edge surfaces. However, the molecular-scale acid-base reactivity of these interfaces remains poorly understood due to the limitations of both experimental resolution and conventional simulations. Here, we employ machine learning potentials with first-principles accuracy to perform nanosecond-scale molecular dynamics simulations of montmorillonite nanoparticles across a range of pH. Our results reveal clear amphoteric behavior: edge sites undergo protonation in acidic environments and deprotonation in basic conditions. Even at neutral pH, spontaneous and directional proton transfer events are common, proceeding via both direct and solvent-mediated pathways. These findings demonstrate that montmorillonite edges are not static arrays of hydroxyl groups but dynamic, proton-conducting networks whose reactivity is modulated by local structure and solution conditions. This work offers a molecular-level framework for understanding proton transport and buffering in clay-water systems, with broad implications for catalysis, ion exchange, and environmental remediation.

physics.chem-ph↗

arXiv2025

How reactive is water at the nanoscale and how to control it?

Nanoconfined water plays a key role in nanofluidics, electrochemistry, and catalysis, yet its reactivity remains a matter of debate. Prior studies have reported both enhanced and suppressed water self-dissociation relative to the bulk, but without a consistent explanation. Here, using enhanced sampling molecular dynamics with machine-learned potentials trained at first-principles accuracy, we investigate dissociation behavior in water confined within 2D slit pores and nanodroplets, using graphene and hexagonal boron nitride as model materials. We find that reactivity is extremely sensitive to water density, confinement width, geometry, material flexibility, and surface chemistry. Despite this complexity, we show that chemical potential -- together with interfacial interactions -- governs dissociation trends and explains the variability observed in prior studies. This thermodynamic perspective reconciles previous contradictions and reveals how nanoscale environments can drastically shift water reactivity. Our findings provide molecular-level insight and offer a design lever for modulating water chemistry at the nanoscale.

physics.chem-ph↗

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

Spontaneous Surface Charging and Janus Nature of the Hexagonal Boron Nitride-Water Interface

Boron, nitrogen and carbon are neighbors in the periodic table and can form strikingly similar twin structures-hexagonal boron nitride (hBN) and graphene-yet nanofluidic experiments demonstrate drastically different water friction on them. We investigate this discrepancy by probing the interfacial water and atomic-scale properties of hBN using surface-specific vibrational spectroscopy, atomic-resolution atomic force microscopy (AFM), and machine learning-based molecular dynamics. Spectroscopy reveals that pristine hBN acquires significant negative charges upon contacting water at neutral pH, unlike hydrophobic graphene, leading to interfacial water alignment and stronger hydrogen bonding. AFM supports that this charging is not defect-induced. pH-dependent measurements suggest OH- chemisorption and physisorption, which simulations validate as two nearly equally stable states undergoing dynamic exchange. These findings challenge the notion of hBN as chemically inert and hydrophobic, revealing its spontaneous surface charging and Janus nature, and providing molecular insights into its higher water friction compared to carbon surfaces.

physics.chem-ph↗

arXiv2025

Protons accumulate at the graphene-water interface

Water's ability to autoionize into hydroxide and hydronium ions profoundly influences surface properties, rendering interfaces either basic or acidic. While it is well-established that protons show an affinity to the air-water interface, a critical knowledge gap exists in technologically relevant surfaces like the graphene-water interface. Here we use machine learning-based simulations with first-principles accuracy to unravel the behavior of the hydroxide and hydronium ions at the graphene-water interface. Our findings reveal that protons accumulate at the graphene-water interface, with the hydronium ion predominantly residing in the first contact layer of water. In contrast, the hydroxide ion exhibits a bimodal distribution, found both near the surface and towards the interior layers. Analysis of the underlying electronic structure reveals local polarization effects, resulting in counterintuitive charge rearrangement. Proton propensity to the graphene-water interface challenges the interpretation of surface experiments and is expected to have far-reaching consequences for ion conductivity, interfacial reactivity, and proton-mediated processes.

physics.chem-ph↗

arXiv2024

Nuclear quantum effects induce superionic proton transport in nanoconfined water

Recent work has suggested that nanoconfined water may exhibit superionic proton transport at lower temperatures and pressures than bulk water. Using first-principles-level simulations, we study the role of nuclear quantum effects in inducing this superionicity in nanoconfined water. We show that nuclear quantum effects increase the ionic conductivity of nanoconfined hexatic water, leading to superionic behaviour at lower temperatures and pressures than previously thought possible. Our work suggests that superionic water may be accessible in graphene nanocapillary experiments.

cond-mat.mtrl-sci↗

arXiv2024

Quasi-one-dimensional hydrogen bonding in nanoconfined ice

The Bernal-Fowler ice rules stipulate that each water molecule in an ice crystal should form four hydrogen bonds. However, in extreme or constrained conditions, the arrangement of water molecules deviates from conventional ice rules, resulting in properties significantly different from bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism in nanoconfined ice phases beyond conventional ice rules. Instead of forming four hydrogen bonds, nanoconfined crystalline ice can form a quasi-one-dimensional hydrogen-bonded structure that exhibits only two hydrogen bonds per water molecule. These structures consist of strongly hydrogen-bonded linear chains of water molecules that zig-zag along one dimension, stabilized by van der Waals interactions that stack these chains along the other dimension. The unusual interplay of hydrogen bonding and van der Waals interactions in nanoconfined ice results in atypical proton behavior such as potential ferroelectric behavior, low dielectric response, and long-range proton dynamics.

cond-mat.stat-mech↗

arXiv2024

Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies

Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide range of technological applications. However, predicting these quantities at first-principles accuracy -- even with the aid of machine learning potentials -- is a challenge that requires sub-kJ/mol accuracy in the potential energy surface and finite-temperature sampling. We present an accurate and data-efficient protocol based on fine-tuning of the foundational MACE-MP-0 model and showcase its capabilities on sublimation enthalpies and physical properties of ice polymorphs. Our approach requires only a few tens of training structures to achieve sub-kJ/mol accuracy in the sublimation enthalpies and sub 1 % error in densities for polymorphs at finite temperature and pressure. Exploiting this data efficiency, we explore simulations of hexagonal ice at the random phase approximation level of theory at experimental temperatures and pressures, calculating its physical properties, like pair correlation function and density, with good agreement with experiments. Our approach provides a way forward for predicting the stability of molecular crystals at finite thermodynamic conditions with the accuracy of correlated electronic structure theory.

cond-mat.mtrl-sci↗