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 substantially different from those of bulk water, as shown, for example, by the anomalously low dielectric constant of water in nanochannels1, near frictionless water flow2 or the possible existence of a square ice phase3. Such properties suggest that nanoconfined water could be engineered for technological applications in nanofluidics4, electrolyte materials5 and water desalination6. Unfortunately, challenges in experimentally characterizing water at the nanoscale and the high cost of first-principles simulations have prevented the molecular-level understanding required to control the behaviour 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 behaviour 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 more than 400 kelvins 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 behaviour under easily accessible conditions.

Tracking single adatoms in liquid in a transmission electron microscope.

Single atoms or ions on surfaces affect processes from nucleation1 to electrochemical reactions2 and heterogeneous catalysis3. Transmission electron microscopy is a leading approach for visualizing single atoms on a variety of substrates4,5. It conventionally requires high vacuum conditions, but has been developed for in situ imaging in liquid and gaseous environments6,7 with a combined spatial and temporal resolution that is unmatched by any other method-notwithstanding concerns about electron-beam effects on samples. When imaging in liquid using commercial technologies, electron scattering in the windows enclosing the sample and in the liquid generally limits the achievable resolution to a few nanometres6,8,9. Graphene liquid cells, on the other hand, have enabled atomic-resolution imaging of metal nanoparticles in liquids10. Here we show that a double graphene liquid cell, consisting of a central molybdenum disulfide monolayer separated by hexagonal boron nitride spacers from the two enclosing graphene windows, makes it possible to monitor, with atomic resolution, the dynamics of platinum adatoms on the monolayer in an aqueous salt solution. By imaging more than 70,000 single adatom adsorption sites, we compare the site preference and dynamic motion of the adatoms in both a fully hydrated and a vacuum state. We find a modified adsorption site distribution and higher diffusivities for the adatoms in the liquid phase compared with those in vacuum. This approach paves the way for in situ liquid-phase imaging of chemical processes with single-atom precision.

The Interplay of Solvation and Polarization Effects on Ion Pairing in Nanoconfined Electrolytes.

The nature of ion-ion interactions in electrolytes confined to nanoscale pores has important implications for energy storage and separation technologies. However, the physical effects dictating the structure of nanoconfined electrolytes remain debated. Here we employ machine-learning-based molecular dynamics simulations to investigate ion-ion interactions with density functional theory level accuracy in a prototypical confined electrolyte, aqueous NaCl within graphene slit pores. We find that the free energy of ion pairing in highly confined electrolytes deviates substantially from that in bulk solutions, observing a decrease in contact ion pairing but an increase in solvent-separated ion pairing. These changes arise from an interplay of ion solvation effects and graphene's electronic structure. Notably, the behavior observed from our first-principles-level simulations is not reproduced even qualitatively with the classical force fields conventionally used to model these systems. The insight provided in this work opens new avenues for predicting and controlling the structure of nanoconfined electrolytes.

Theoretical infrared spectroscopy of protonated methane isotopologues.

The vibrational spectroscopy of protonated methane and its mixed hydrogen/deuterium isotopologues remains a challenge to both experimental and computational spectroscopy due to the iconic floppiness of CH5+. Here, we compute the finite-temperature broadband infrared spectra of CH5+ and all its isotopologues, i.e. CHnD5-n+ up to CD5+, from path integral molecular dynamics in conjunction with interactions and dipoles computed consistently at CCSD(T) coupled cluster accuracy. The potential energy and dipole moment surfaces have been accurately represented in full dimensionality in terms of high-dimensional neural networks. The resulting computational efficiency allows us to establish CCSD(T) accuracy at the level of converged path integral simulations. For all six isotopologues, the computed broadband spectra compare very favorably to the available experimental broadband spectra obtained from laser induced reactions action vibrational spectroscopy. The current approach is found to consistently and significantly improve on previous calculations of these broadband vibrational spectra and defines the new cutting-edge for what has been dubbed the "enfant terrible" of molecular spectroscopy in view of its pronounced large-amplitude motion that involves all intramolecular degrees of freedom.

Crumbling crystals: on the dissolution mechanism of NaCl in water.

Dissolution of ionic salts in water is ubiquitous, particularly for 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 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 area to volume of the crystal. Overall, dissolution comprises a series of highly dynamical microscopic sub-processes, resulting in an inherently stochastic mechanism. These atomistic level insights contribute to the 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.

The wetting of H2O by CO2.

Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those of the bulk. The CO2-H2O 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 CO2-H2O interface, particularly concerning the interfacial tension and phase behavior of CO2 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 CO2-H2O interface. Interfacial tensions are predicted from 1 to 500 bars and found to be in close agreement with experiment at pressures for which experimental data are available. Structural analyses indicate the buildup of an adsorbed, saturated CO2 film forming at a low pressure (20 bars) with properties similar to those of the bulk liquid, but preferential perpendicular alignment with respect to the interface. The CO2 monolayer buildup 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.

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 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 afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and 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.

Onset of Rotational Decoupling for a Molecular Ion Solvated in Helium: From Tags to Rings and Shells.

Little is known about how rotating molecular ions interact with multiple ^{4}He atoms and how this relates to microscopic superfluidity. Here, we use infrared spectroscopy to investigate ^{4}He_{N}⋯H_{3}O^{+} complexes and find that H_{3}O^{+} undergoes dramatic changes in rotational behavior as ^{4}He atoms are added. We present evidence of clear rotational decoupling of the ion core from the surrounding helium for N>3, with sudden changes in rotational constants at N=6 and 12. In sharp contrast to studies on small neutral molecules microsolvated in helium, accompanying path integral simulations show that an incipient superfluid effect is not needed to account for these findings.

Quantum Nuclear Delocalization and its Rovibrational Fingerprints.

Quantum mechanics dictates that nuclei must undergo some delocalization. In this work, emergence of quantum nuclear delocalization and its rovibrational fingerprints are discussed for the case of the van der Waals complex HHe 3 + ${{\rm{HHe}}_3^ + }$ . The equilibrium structure of HHe 3 + ${{\rm{HHe}}_3^ + }$ is planar and T-shaped, one He atom solvating the quasi-linear He-H+ -He core. The dynamical structure of HHe 3 + ${{\rm{HHe}}_3^ + }$ , in all of its bound states, is fundamentally different. As revealed by spatial distribution functions and nuclear densities, during the vibrations of the molecule the solvating He is not restricted to be in the plane defined by the instantaneously bent HHe 2 + ${{\rm{HHe}}_2^ + }$ chomophore, but freely orbits the central proton, forming a three-dimensional torus around the HHe 2 + ${{\rm{HHe}}_2^ + }$ chromophore. This quantum delocalization is observed for all vibrational states, the type of vibrational excitation being reflected in the topology of the nodal surfaces in the nuclear densities, showing, for example, that intramolecular bending involves excitation along the circumference of the torus.

Neural network interaction potentials for para-hydrogen with flexible molecules.

The study of molecular impurities in para-hydrogen (pH2) 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 only one or very few pH2, the microsolvation regime for intermediate particle numbers, and matrix isolation with many solvent molecules. However, the fundamental coupling between the bosonic pH2 environment and the (ro-)vibrational motion of molecular impurities remains poorly understood. Quantum simulations can, in principle, provide the necessary atomistic insight, but they require very accurate descriptions of the involved interactions. Here, we present a data-driven approach for the generation of impurity⋯pH2 interaction potentials based on machine learning techniques, which retain the full flexibility of the dopant species. 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 pH2. 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, explicitly correlated coupled cluster single, double, and scaled perturbative triples, 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 corresponding H2O⋯pH2 and H3O+⋯pH2 interactions. Using path integral simulations, we show for the hydronium cation, H3O+, that umbrella-like tunneling inversion has a strong impact on the first and second pH2 microsolvation shells. The automated and data-driven nature of our protocol opens the door to the study of bosonic pH2 quantum solvation for a wide range of embedded impurities.

Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer.

A previously published neural network potential for the description of protonated water clusters up to the protonated water tetramer, H+(H2O)4, at an essentially converged coupled cluster accuracy [C. Schran, J. Behler, and D. Marx, J. Chem. Theory Comput. 16, 88 (2020)] is applied to the protonated water hexamer, H+(H2O)6-a system that the neural network has never seen before. Although being in the extrapolation regime, it is shown that the potential not only allows for quantum simulations from ultra-low temperatures ∼1 K up to 300 K but is also able to describe the new system very accurately compared to explicit coupled cluster calculations. This transferability of the model is rationalized by the similarity of the atomic environments encountered for the larger cluster compared to the environments in the training set of the model. Compared to the interpolation regime, the quality of the model is reduced by roughly one order of magnitude, but most of the difference to the coupled cluster reference comes from global shifts of the potential energy surface, while local energy fluctuations are well recovered. These results suggest that the application of neural network potentials in extrapolation regimes can provide useful results and might be more general than usually thought.

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.

Converged quantum simulations of reactive solutes in superfluid helium: The Bochum perspective.

Superfluid helium has not only fascinated scientists for centuries but is also the ideal matrix for the investigation of chemical systems under ultra-cold conditions in helium nanodroplet isolation experiments. Together with related experimental techniques such as helium tagging photodissociation spectroscopy, these methods have provided unique insights into many interesting systems. Complemented by theoretical work, they were additionally able to greatly expand our general understanding of manifestations of superfluid behavior in finite sized clusters and their response to molecular impurities. However, most theoretical studies up to now have not included the reactivity and flexibility of molecular systems embedded in helium. In this perspective, the theoretical foundation of simulating fluxional molecules and reactive complexes in superfluid helium is presented in detail. Special emphasis is put on recent developments for the converged description of both the molecular interactions and the quantum nature of the nuclei at ultra-low temperatures. As a first step, our hybrid path integral molecular dynamics/bosonic path integral Monte Carlo method is reviewed. Subsequently, methods for efficient path integral sampling tailored for this hybrid coupling scheme are discussed while also introducing new developments to enhance the accurate incorporation of the solute⋯solvent coupling. Finally, highly accurate descriptions of the interactions in solute⋯helium systems using machine learning techniques are addressed. Our current automated and adaptive fitting procedures to parameterize high-dimensional neural network potentials for both the full-dimensional potential energy surface of solutes and the solute⋯solvent interaction potentials are concisely presented. They are demonstrated to faithfully represent many-body potential functions able to describe chemically complex and reactive solutes in helium environments seamlessly from one He atom up to bulk helium at the accuracy level of coupled cluster electronic structure calculations. Together, these advances allow for converged quantum simulations of fluxional and reactive solutes in superfluid helium under cryogenic conditions.

Quantum nature of the hydrogen bond from ambient conditions down to ultra-low temperatures.

Many experimental techniques such as tagging photodissociation and helium nanodroplet isolation spectroscopy operate at very low temperatures in order to investigate hydrogen bonding. To elucidate the differences between such ultra-cold and usual ambient conditions, different hydrogen bonded systems are studied systematically from 300 K down to about 1 K using path integral simulations that explicitly consider both the quantum nature of the nuclei and thermal fluctuations. For this purpose, finite sized water clusters, specifically the water dimer and hexamer, protonated water clusters including the Zundel and Eigen complexes, as well as hexagonal ice as a condensed phase representative are compared directly as a function of temperature. While weaker hydrogen bonds, as present in the neutral systems, show distinct structural differences between ambient conditions and the ultra-cold regime, the stronger hydrogen bonds of the protonated water clusters are less perturbed by temperature compared to their quantum ground state. In all the studied systems, the quantum delocalization of the nuclei is found to vary drastically with temperature. Interestingly, upon reaching temperatures of about 1 K, the spatial quantum delocalization of the heavy oxygens approaches that of the protons for relatively weak spatial constraints, and even significantly exceeds the latter in the case of the centered hydrogen bond in the Zundel complex. These findings are relevant for comparisons between experiments on hydrogen bonding carried out under ultra-cold versus ambient conditions as well as to understand quantum delocalization phenomena of nuclei by seamlessly extending our insights into noncovalent interactions down to ultra-low temperatures.

Force-Induced Catastrophes on Energy Landscapes: Mechanochemical Manipulation of Downhill and Uphill Bifurcations Explains the Ring-Opening Selectivity of Cyclopropanes.

The mechanochemistry of ring-opening reactions of cyclopropane derivatives turns out to be unexpectedly rich and puzzling. After showing that a rare so-called uphill bifurcation in the case of trans-gem-difluorocyclopropane turns into a downhill bifurcation upon substitution of fluorine by chlorine, bromine, and iodine in the thermal activation limit, the dichloro derivative is studied systematically in the realm of mechanochemical activation. Detailed exploration of the force-transformed potential energy surface of trans-gem-dichlorocyclopropane in terms of Dijkstra path analysis unveils a hitherto unknown topological catastrophe where the global shape of the energy landscape is fundamentally changed. From thermal activation up to moderately large forces, it is an uphill bifurcation that decides about dis- versus conrotatory ring-opening followed by separate transition states along both pathways. Above a critical force, the two distinct transition states merge to yield a single transition state such that the decision about the dis- versus conrotatory ring-opening process is taken at a newly established downhill bifurcation. The discovery of a force-induced qualitative change of the topology of a reaction network vastly transcends the previous understanding of the ring-opening reaction of this species. It would be astonishing to not discover a wealth of such catastrophes for mechanochemically activated reactions, which will greatly extend the known opportunities to manipulate chemical reaction networks.

High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium.

The design of accurate helium-solute interaction potentials for the simulation of chemically complex molecules solvated in superfluid helium has long been a cumbersome task due to the rather weak but strongly anisotropic nature of the interactions. We show that this challenge can be met by using a combination of an effective pair potential for the He-He interactions and a flexible high-dimensional neural network potential (NNP) for describing the complex interaction between helium and the solute in a pairwise additive manner. This approach yields an excellent agreement with a mean absolute deviation as small as 0.04 kJ mol-1 for the interaction energy between helium and both hydronium and Zundel cations compared with coupled cluster reference calculations with an energetically converged basis set. The construction and improvement of the potential can be performed in a highly automated way, which opens the door for applications to a variety of reactive molecules to study the effect of solvation on the solute as well as the solute-induced structuring of the solvent. Furthermore, we show that this NNP approach yields very convincing agreement with the coupled cluster reference for properties like many-body spatial and radial distribution functions. This holds for the microsolvation of the protonated water monomer and dimer by a few helium atoms up to their solvation in bulk helium as obtained from path integral simulations at about 1 K.

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 liquidlike and clustered gaslike supercritical water.

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. 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.

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 rationalize 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 wave function based machine learning potentials with the random phase approximation (RPA) and second order Møller-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.

Infrared Spectra at Coupled Cluster Accuracy from Neural Network Representations.

Infrared spectroscopy is key to elucidating molecular structures, monitoring reactions, and observing 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.