arXiv subjects

Christoph Schran

Publications and source records attributed to Christoph Schran.

At least 37 records · Page 2Linked to original sources

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

PLUMED Tutorials: a collaborative, community-driven learning ecosystem

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources.

physics.ed-ph↗

arXiv2024

Introduction to machine learning potentials for atomistic simulations

Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This paper aims to provide an overview and introduction into machine learning potentials and their practical application to scientific problems. We provide a systematic guide for developing machine learning potentials, reviewing chemical descriptors, regression models, data generation and validation approaches. We begin with an emphasis on the earlier generation of models, such as high-dimensional neural network potentials (HD-NNPs) and Gaussian approximation potential (GAP), to provide historical perspective and guide the reader towards the understanding of recent developments, which are discussed in detail thereafter. Furthermore, we refer to relevant expert reviews, open-source software, and practical examples - further lowering the barrier to exploring these methods. The paper ends with selected showcase examples, highlighting the capabilities of machine learning potentials and how they can be applied to push the boundaries in atomistic simulations.

physics.chem-ph↗

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

The Wetting of H$_2$O by CO$_2$

Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those of the bulk. The CO$_2$-H$_2$O interface, in particular, has been the subject of a number of studies on account of its importance for the carbon life cycle as well as carbon capture and sequestration schemes. Despite this attention, there remain a number of open questions on the nature of the CO$_2$-H$_2$O interface, particularly concerning the interfacial tension and phase behavior of CO$_2$ at the interface. In this paper, we seek to address these ambiguities using ab initio-quality simulations. Harnessing the benefits of machine-learned potentials and enhanced statistical sampling methods, we present an ab initio-level description of the CO$_2$-H$_2$O interface. Interfacial tensions are predicted from 1-500 bar and found to be in close agreement with experiment at the pressures for which experimental data is available. Structural analyses indicate the build-up of an adsorbed, saturated CO$_2$ film forming at low pressure (20 bar) with properties similar to those of the bulk liquid, but preferential perpendicular alignment with respect to the interface. CO$_2$ monolayer build-up coincides with a reduced structuring of water molecules close to the interface. This study highlights the predictive nature of machine-learned potentials for complex macroscopic properties of biphasic interfaces, and the mechanistic insight obtained into carbon dioxide aggregation at the water interface is of high relevance for geoscience, climate research, and materials science.

cond-mat.mtrl-sci↗

arXiv2024

To Pair or not to Pair? Machine-Learned Explicitly-Correlated Electronic Structure for NaCl in Water

The extent of ion pairing in solution is an important phenomenon to rationalise transport and thermodynamic properties of electrolytes. A fundamental measure of this pairing is the potential of mean force (PMF) between solvated ions. The relative stabilities of the paired and solvent shared states in the PMF and the barrier between them are highly sensitive to the underlying potential energy surface. However direct application of accurate electronic structure methods is challenging, since long simulations are required. We develop wavefunction based machine learning potentials with the Random Phase Approximation (RPA) and second order Moller-Plesset (MP2) perturbation theory for the prototypical system of Na and Cl ions in water. We show both methods in agreement, predicting the paired and solvent shared states to have similar energies (within 0.2 kcal/mol). We also provide the same benchmarks for different DFT functionals as well as insight into the PMF based on simple analyses of the interactions in the system.

physics.chem-ph↗

arXiv2023

Manifestations of Local Supersolidity of $^{4}$He around a Charged Molecular Impurity

A frozen, solid helium core, dubbed snowball, is typically observed around cations in liquid helium. Here we discover, using path integral simulations, that around a cationic molecular impurity, protonated methane, the $^4$He atoms are indeed strongly localized akin to snowballs but still participate in vivid bosonic exchange induced by the ro-vibrational motion of the impurity. Such combination of solid-like order with pronounced superfluid response in the first helium shell indicates that manifestations of local supersolid behavior of $^4$He can be induced -- and probed experimentally -- by charged molecules.

physics.chem-ph↗

arXiv2022

Understanding the anomalously low dielectric constant of confined water: an ab initio study

Recent experiments have shown that the out-of-plane dielectric constant of water confined in nanoslits of graphite and hexagonal boron nitride (hBN) is vanishingly small. Despite extensive effort based mainly on classical force-field molecular dynamics (FFMD) approaches, the origin of this phenomenon is under debate. Here we used ab initio molecular dynamics simulations (AIMD) and AIMD-trained machine learning potentials to explore the structure and electronic properties of water confined inside graphene and hBN slits. We found that the reduced dielectric constant arises mainly from the anti-parallel alignment of the water dipoles in the perpendicular direction to the surface in the first two water layers near the solid interface. Although the water molecules retain liquid-like mobility, the interfacial layers exhibit a net ferroelectric ordering and constrained hydrogen-bonding orientations which lead to much reduced polarization fluctuations in the out-of-plane direction at room temperature. Importantly, we show that this effect is independent of the distance between the two confining surfaces of the slit, and it originates in the spontaneous polarization of interfacial water. Our calculations also show no significant variations in the structure and polarization of water near graphene and hBN, despite their different electronic structures. These results are important as they offer new insight into a property of water that plays a critical role in the long-range interactions between surfaces, the electric double-layer formation, ion solvation and transport, as well as biomolecular functioning.

physics.chem-ph↗

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

State-resolved infrared spectrum of the protonated water dimer: Revisiting the characteristic proton transfer doublet peak

The infrared (IR) spectra of protonated water clusters encode precise information on the dynamics and structure of the hydrated proton. However, the strong anharmonic coupling and quantum effects of these elusive species remain puzzling up to the present day. Here, we report unequivocal evidence that the interplay between the proton transfer and the water wagging motions in the protonated water dimer (Zundel ion) giving rise to the characteristic doublet peak is both more complex and more sensitive to subtle energetic changes than previously thought. In particular, hitherto overlooked low-intensity satellite peaks in the experimental spectrum are now unveiled and mechanistically assigned. Our findings rely on the comparison of IR spectra obtained using two highly accurate potential energy surfaces in conjunction with highly accurate state-resolved quantum simulations. We demonstrate that these high-accuracy simulations are important for providing definite assignments of the complex IR signals of fluxional molecules.

physics.chem-ph↗

arXiv2022

Infrared spectra at coupled cluster accuracy from neural network representations

Infrared spectroscopy is key to elucidate molecular structures, monitor reactions and observe conformational changes, while providing information on both structural and dynamical properties. This makes the accurate prediction of infrared spectra based on first-principle theories a highly desirable pursuit. Molecular dynamics simulations have proven to be a particularly powerful approach for this task, albeit requiring the computation of energies, forces and dipole moments for a large number of molecular configurations as a function of time. This explains why highly accurate first principles methods, such as coupled cluster theory, have so far been inapplicable for the prediction of fully anharmonic vibrational spectra of large systems at finite temperatures. Here, we push cutting-edge machine learning techniques forward by using neural network representations of energies, forces and in particular dipoles to predict such infrared spectra fully at "gold standard" coupled cluster accuracy as demonstrated for protonated water clusters as large as the protonated water hexamer, in its extended Zundel configuration. Furthermore, we show that this methodology can be used beyond the scope of the data considered during the development of the neural network models, allowing for the computation of finite-temperature infrared spectra of large systems inaccessible to explicit coupled cluster calculations. This substantially expands the hitherto existing limits of accuracy, speed and system size for theoretical spectroscopy and opens up a multitude of avenues for the prediction of vibrational spectra and the understanding of complex intra- and intermolecular couplings.

physics.chem-ph↗

arXiv2022

The first-principles phase diagram of monolayer nanoconfined water

Water in nanoscale cavities is ubiquitous and of central importance to everyday phenomena in geology and biology. However, the properties of nanoscale water can be remarkably different from bulk, as shown e.g., by the anomalously low dielectric constant of water in nanochannels [1], near frictionless water flow [2], or the possible existence of a square ice phase [3]. Such properties suggest that nanoconfined water could be engineered for technological applications in nanouidics [4], electrolyte materials [5], and water desalination [6]. Unfortunately, challenges in experimentally characterising water on the nanoscale and the high cost of first-principles simulations have prevented the molecular level understanding required to control the behavior of water. Here we combine a range of computational approaches to enable a first-principles level investigation of a single layer of water within a graphene-like channel. We find that monolayer water exhibits surprisingly rich and diverse phase behavior that is highly sensitive to temperature and the van der Waals pressure acting within the nanochannel. In addition to multiple molecular phases with melting temperatures varying non-monotonically by over 400 degrees with pressure, we predict a hexatic phase, which is an intermediate between a solid and a liquid, and a superionic phase with a high electrical conductivity exceeding that of battery materials. Notably, this suggests that nanoconfinement could be a promising route towards superionic behavior at easily accessible conditions.

cond-mat.mtrl-sci↗

arXiv2022

Neural Network Interaction Potentials for para-Hydrogen with Flexible Molecules

The study of molecular impurities in $para$-hydrogen ($p$H$\rm_2$) clusters is key to push forward our understanding of intra- and intermolecular interactions including their impact on the superfluid response of this bosonic quantum solvent. This includes tagging with one or very few $p$H$\rm_2$, the microsolvation regime, and matrix isolation. However, the fundamental coupling between the bosonic $p$H$\rm_2$ environment and the (ro-)vibrational motion of molecular impurities remains poorly understood. Quantum simulations can in provide the necessary atomistic insight, but very accurate descriptions of the involved interactions are required. Here, we present a data-driven approach for the generation of $impurity\cdots p$H$\rm_2$ interaction potentials based on machine learning techniques which retain the full flexibility of the impurity. We employ the well-established adiabatic hindered rotor (AHR) averaging technique to include the impact of the nuclear spin statistics on the symmetry-allowed rotational quantum numbers of $p$H$\rm_2$. Embedding this averaging procedure within the high-dimensional neural network potential (NNP) framework enables the generation of highly-accurate AHR-averaged NNPs at coupled cluster accuracy, namely CCSD(T$^*$)-F12a/aVTZcp in an automated manner. We apply this methodology to the water and protonated water molecules, as representative cases for quasi-rigid and highly-flexible molecules respectively, and obtain AHR-averaged NNPs that reliably describe the H$\rm _2$O$\cdots p$H$\rm_2$ and H$\rm _3$O$^+\cdots p$H$\rm_2$ interactions. Using path integral simulations we show for the hydronium cation that umbrella-like tunneling inversion has a strong impact on the first and second $p$H$\rm_2$ microsolvation shells. The data-driven nature of our protocol opens the door to the study of bosonic $p$H$\rm_2$ quantum solvation for a wide range of embedded impurities.

physics.chem-ph↗

arXiv2022

Tracking single atoms in a liquid environment

The chemical behaviour of single metal atoms largely depends on the local coordination environment, including interactions with the substrate and with the surrounding gas or liquid. However, the key instrumentation for studying such systems at the atomic scale generally requires high vacuum conditions, limiting the degree to which the aforementioned environmental parameters can be investigated. Here we develop a new platform for transmission electron microscopy investigation of single metal atoms in liquids and study the dynamic behaviour of individual platinum atoms on the surface of a single layer MoS2 crystal in water. To achieve the record single atom resolution, we introduce a double liquid cell based on a 2D material heterostructure, which allows us to submerge an atomically thin membrane with liquid on both sides while maintaining the total specimen thickness of only ~ 70 nm. By comparison with an identical specimen imaged under high vacuum conditions, we reveal drastic differences in the single atom resting sites and atomic hopping behaviour, demonstrating that in situ imaging conditions are essential to gain complete understanding of the chemical activity of individual atoms. These findings pave the way for in situ liquid imaging of chemical processes with single atom precision.

cond-mat.mtrl-sci↗

arXiv2022

Water flow in single-wall nanotubes: Oxygen makes it slip, hydrogen makes it stick

Experimental measurements have reported ultra-fast and radius-dependent water transport in carbon nanotubes which are absent in boron nitride nanotubes. Despite considerable effort, the origin of this contrasting (and fascinating) behaviour is not understood. Here, with the aid of machine learning-based molecular dynamics simulations that deliver first-principles accuracy, we investigate water transport in single-wall carbon and boron nitride nanotubes. Our simulations reveal a large, radius-dependent hydrodynamic slippage on both materials with water experiencing indeed a $\approx 5$ times lower friction on carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms across the two materials reveals that the fast water transport on carbon is governed by facile oxygen motion, whereas the higher friction on boron nitride arises from specific hydrogen-nitrogen interactions. This work not only delivers a clear reference of unprecedented accuracy for water flow in single-wall nanotubes, but also provides detailed mechanistic insight into its radius and material dependence for future technological application.

cond-mat.mtrl-sci↗

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

Correlated Particle Motion and THz Spectral Response of Supercritical Water

Molecular dynamics simulations of supercritical water reveal distinctly different distance-dependent modulations of dipolar response and correlations in particle motion compared to ambient conditions. The strongly perturbed H-bond network of water at supercritical conditions allows for considerable translational and rotational freedom of individual molecules. These changes give rise to substantially different infrared spectra and vibrational density of states at THz frequencies for densities above and below the Widom line that separates percolating liquid-like and clustered gas-like supercritical water.

physics.chem-ph↗