Acids at the Edge: Why Nitric and Formic Acid Dissociations at Air-Water Interfaces Depend on Depth and on Interface Specific Area

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 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 further away from it. 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.

Ab initio deep neural network simulations reveal that carbonic acid dissociation is dominated by minority cis-trans conformers

Carbonic acid (H2CO3), rather than water, serves as the primary protonating buffer regulating pH in biological systems and oceans. Its dissociation dynamics, driven by three conformers-cis-cis (CC), cis-trans (CT), and trans-trans (TT)-pose substantial experimental and theoretical challenges. Using deep potential molecular dynamics simulations with ab initio accuracy, we explored the dissociation dynamics of H2CO3 in solution on the nanosecond timescale. While the CC conformer is the most abundant, the CT conformer is the dominant proton donor. This enhanced deprotonation ability arises from the CT conformer's involvement in more hydrogen-bonding ring structures, enabling diverse proton transfer pathways, and its greater electronic asymmetry, which increases hydrophilicity and destabilizes the hydroxyl group. Furthermore, protons dissociated from the CT conformer demonstrate a stronger preference for the homing pathway. Our findings underscore the critical role of the topology and electronic properties of the CT conformer in aqueous H2CO3 dissociation and proton transfer.

Acidifying the Madrid-2019 force field: A rigid model for H3O+ with scaled charges

A classical and rigid force field for the oxonium cation, H3O+, optimized in solutions of TIP4P/2005 water, is introduced. While the charges of both H3O+ and the selected counteranions (i.e., Cl-, Br-, I-, and NO3-) are scaled by a factor of 0.85, following the philosophy of the so-called Madrid-2019 model for ions, the charge distribution of H3O+ was derived within the framework of the self-consistent atomic dipole-corrected Hirshfeld approach. Considering the simplicity of the model, the agreement between experimental data and molecular dynamics simulation results for the curvature of the solution density as a function of the solute concentration is remarkable. However, limitations persist in capturing ion-pairing behavior and long-range hydrogen-bonding dynamics in polyatomic systems. We found that a scaled charge of 0.85e provides an accurate description of the local structure of hydrogen halides but is detrimental to predicting the viscosity of the solution. The opposite effect is observed for HNO3. Nonetheless, the newly optimized potential parameters for H3O+ expand the family of ions with scaled charges in the Madrid-2019 force field, providing a computationally efficient and versatile platform to study electrolyte solutions in acidic environments. These findings contribute to the advancement of molecular modeling techniques and to improving our understanding of the interplay between local structure (solvation, ion pairing) and transport properties in complex systems.

Revealing the photochemical pathways of nitrate in water through first-principles simulations

The nitrate anion (NO3-) is abundant in environmental aqueous phases, including aerosols, surface waters, and snow, where its photolysis releases nitrogen oxides back into the atmosphere. Nitrate photolysis occurs via two channels: (1) the formation of NO2 and O- and (2) the formation of NO2- and O(3P). The occurrence of two reaction channels with very low quantum yield (∼1%) highlights the critical role of the solvation environment and spin-forbidden electronic transitions, which remain unexplained at the molecular level. We investigate the two photolysis channels in water using quantum chemical calculations and first-principles molecular dynamics simulations with hybrid density functional theory and enhanced sampling. We find that spin-forbidden absorption to the triplet state (T1) is possible but occurs at a rate ∼15 times weaker than the spin-allowed transition to the singlet state (S1). A metastable solvation cage complex requires additional thermal energy to dissociate the N-O bond, allowing for recombination or non-radiative deactivation. Our results explain the temperature dependence of photolysis, linked to hydrogen bond rearrangement in the solvation shell. This work provides new molecular insights into nitrate photolysis and its low quantum yield under environmental conditions.

Why Proton Grotthuss Diffusion Slows down at the Air-Water Interface while Water Diffusion Accelerates

Excess proton diffusion at aqueous interfaces is crucial for applications including electrocatalysis, aerosol chemistry, and biological energy conversion. While interfaces have been proposed as pathways for channeling protons, proton diffusion at interfaces remains far less understood than in the bulk. Here we focus on the air-water interface and use density functional theory-based deep potential molecular dynamics simulations to reveal the contrasting interface's impacts: excess proton diffusion slows down compared to the bulk, while water diffusion accelerates. This contrast stems from reduced hydrogen-bond coordination at the interface, which facilitates water diffusion and transient unstable proton rattling but impedes the stable proton hops central to Grotthuss diffusion. As a result, at the interface, excess protons and water molecules diffuse at comparable rates, in stark departure from bulk behavior. This mechanistic insight delineates distinct limiting regimes for bulk-enhanced interfacial proton diffusion, with important implications for interfacial chemistry.

Propensity of Water Self-Ions at Air(Oil)-Water Interfaces Revealed by Deep Potential Molecular Dynamics with Enhanced Sampling

The preference of water self-ions (hydronium and hydroxide) toward air/oil-water interfaces is one of the hottest topics in water research due to its importance for understanding properties, phenomena, and reactions of interfaces. In this work, we performed enhanced-sampling molecular dynamics simulations based on state-of-the-art neural network potentials with approximate M06-2X accuracy to investigate the propensity of hydronium and hydroxide ions at air/oil(decane)-water interfaces, which can simultaneously describe well the water autoionization process forming these ions, the recombination of ions, and the ionic distribution along the normal distance to the interface by employing a set of appropriate Voronoi collective variables. A stable ionic double-layer distribution is observed near the air-water interface, while the distribution is different at oil-water interfaces, where hydronium tends to be repelled from the interface into the bulk water, whereas hydroxide, with an interfacial stabilization free energy of -0.6 kcal/mol, is enriched in the interfacial layer. Through simulations of oil droplets in water, we further reveal that the interfacial propensity of hydroxide ions is caused by the positive charge distribution of the oil-water interface contributed by hydrogens of the dangling OH bonds of the interfacial water layer and the outermost layer decane molecules lying flat on the droplet. The present results may aid in understanding the acid-base nature of water interfaces with wide applications.

ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials

The emergence of artificial intelligence is profoundly impacting computational chemistry, particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional potential energy surface representations, MLIPs overcome the conventional computational scaling limitations by offering an effective combination of accuracy and efficiency for calculating atomic energies and forces to be used in molecular simulations. These MLIPs have significantly enhanced molecular simulations across various applications, including large-scale simulations of materials, interfaces, chemical reactions, and beyond. Despite these advances, the construction of training datasets-a critical component for the accuracy of MLIPs-has not received proportional attention, especially in the context of chemical reactivity, which depends on rare barrier-crossing events that are not easily included in the datasets. Here we address this gap by introducing ArcaNN, a comprehensive framework designed for generating training datasets for reactive MLIPs. ArcaNN employs a concurrent learning approach combined with advanced sampling techniques to ensure an accurate representation of high-energy geometries. The framework integrates automated processes for iterative training, exploration, new configuration selection, and energy and force labeling, all while ensuring reproducibility and documentation. We demonstrate ArcaNN's capabilities through two paradigm reactions: a nucleophilic substitution and a Diels-Alder reaction. These examples showcase its effectiveness, the uniformly low error of the resulting MLIP everywhere along the chemical reaction coordinate, and its potential for broad applications in reactive molecular dynamics. Finally, we provide guidelines for assessing the quality of MLIPs in reactive systems.

Unveiling the Role of Solvent in Solution Phase Chemical Reactions using Deep Potential-Based Enhanced Sampling Simulations

We have used a deep learning-based active learning strategy to develop ab initio level accurate machine-learned (ML) potential for a solution-phase reactive system. Using this ML potential, we carried out enhanced sampling simulations to sample the reaction process efficiently. Multiple transitions between the reactant and product states allowed us to calculate the converged free energy surface for the reaction. As a prototypical example, we have investigated the Menshutkin reaction, a classic bimolecular nucleophilic substitution reaction (SN2) in aqueous medium. Our analyses revealed that water stabilizes the ionic product state by enhanced solvation, facilitating the reaction and making it more spontaneous. Our approach expands the scope of studying the chemical reaction under realistic conditions, such as explicit solvents at finite temperatures, closely mimicking experiments.

Spontaneous Molecular Bromine Production in Sea-Salt Aerosols

Bromine chemistry is responsible for the catalytic ozone destruction in the atmosphere. The heterogeneous reactions of sea-salt aerosols are the main abiotic sources of reactive bromine in the atmosphere. Here, we present a novel mechanism for the activation of bromide ions (Br-) by O2 and H2O in the absence of additional oxidants. The laboratory and theoretical calculation results demonstrated that under dark conditions, Br-, O2 and H3O+ could spontaneously generate Br and HO2 radicals through a proton-electron transfer process at the air-water interface and in the liquid phase. Our results also showed that light and acidity could significantly promote the activation of Br- and the production of Br2. The estimated gaseous Br2 production rate was up to 1.55×1010 molecules cm-2 ⋅ s-1 under light and acidic conditions; these results showed a significant contribution to the atmospheric reactive bromine budget. The reactive oxygen species (ROS) generated during Br- activation could promote the multiphase oxidation of SO2 to produce sulfuric acid, while the increase in acidity had a positive feedback effect on Br- activation. Our findings highlight the crucial role of the proton-electron transfer process in Br2 production; here, H3O+ facilitates the activation of Br- by O2, serves as a significant source of atmospheric reactive bromine and exerts a profound impact on the atmospheric oxidation capacity.

Exploration of the intracellular chiral metabolome in pediatric BCP-ALL: a pilot study investigating the metabolic phenotype of IgH locus aberrations

Background and aims

Aberrations in the immunoglobulin heavy chain (IgH) locus are associated with poor prognosis in pediatric precursor B-cell acute lymphoblastic leukemia (BCP-ALL) patients. The primary objective of this pilot study is to enhance our understanding of the IgH phenotype by exploring the intracellular chiral metabolome.

Materials and methods

Leukemia cells were isolated from the bone marrow of BCP-ALL pediatric patients at diagnosis. The samples' metabolome and transcriptome were characterized using untargeted chiral metabolomic and next-generation sequencing transcriptomic analyses.

Results

For the first time D- amino acids were identified in the leukemic cells' intracellular metabolome from the bone marrow niche. Chiral metabolic signatures at diagnosis was indicative of a resistant phenotype. Through integrated network analysis and Pearson correlation, confirmation was obtained regarding the association of the IgH phenotype with several genes linked to poor prognosis.

Conclusion

The findings of this study have contributed to the understanding that the chiral metabolome plays a role in the poor prognosis observed in an exceptionally rare patient cohort. The findings include elevated D-amino acid incorporation in the IgH group, the emergence of several unknown, potentially enantiomeric, metabolites, and insights into metabolic pathways that all warrant further exploration.

Multi-Level Protocol for Mechanistic Reaction Studies Using Semi-Local Fitted Potential Energy Surfaces

In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (SN2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method's performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies.

The Structure of Carbon Dioxide at the Air-Water Interface and its Chemical Implications

The efficient reduction of CO2 into valuable products is a challenging task in an international context marked by the climate change crisis and the need to move away from fossil fuels. Recently, the use of water microdroplets has emerged as an interesting reaction media where many redox processes which do not occur in conventional solutions take place spontaneously. Indeed, several experimental studies in microdroplets have already been devoted to study the reduction of CO2 with promising results. The increased reactivity in microdroplets is thought to be linked to unique electrostatic solvation effects at the air-water interface. In the present work, we report a theoretical investigation on this issue for CO2 using first-principles molecular dynamics simulations. We show that CO2 is stabilized at the interface, where it can accumulate, and that compared to bulk water solution, its electron capture ability is larger. Our results suggest that reduction of CO2 might be easier in interface-rich systems such as water microdroplets, which is in line with early experimental data and indicate directions for future laboratory studies. The effect of other relevant factors which could play a role in CO2 reduction potential is discussed.

A Vacuum Ultraviolet Photoionization Mass Spectrometry and Density Functional Calculation Study of Formic Acid-Water Clusters

The interaction between formic acid (FA) and water (W) holds significant importance in various chemical processes. Our study combines vacuum-ultraviolet photoionization mass spectrometry with density functional calculations to investigate formic acid water clusters generated in supersonic molecular beams. The mass spectra obtained reveal the formation of protonated clusters as the major product. Enhanced intensities are observed in the mass spectra for a number of clusters holding the following composition, FA1W5H+, FA2W4H+, FA3W3H+, FA4W2H+, FA5W1H+ and FA6W2H+ compared to their neighbors with one less or one more water component. Our calculations shed light on these potentially stable structures, highlighting cyclic arrangements with molecules enclosed within the ring as the most stable structures, and demonstrate a decrease in the stability upon the addition of a water molecule. Comparing experimental appearance energies with calculated ionization energies suggests that fragmentation can occur from clusters of various sizes.

Double-Layer Distribution of Hydronium and Hydroxide Ions in the Air-Water Interface

The acid-base nature of the aqueous interface has long been controversial. Most macroscopic experiments suggest that the air-water interface is basic based on the detection of negative charges at the interface that indicates the enrichment of hydroxides (OH-), whereas microscopic studies mostly support the acidic air-water interface with the observation of hydronium (H3O+) accumulation in the top layer of the interface. It is crucial to clarify the interfacial preference of OH- and H3O+ ions for rationalizing the debate. In this work, we perform deep potential molecular dynamics simulations to investigate the preferential distribution of OH- and H3O+ ions at the aqueous interfaces. The neural network potential energy surface is trained based on density functional theory calculations with the SCAN functional, which can accurately describe the diffusion of these two ions both in the interface and in the bulk water. In contrast to the previously reported single ion enrichment, we show that both OH- and H3O+ surprisingly prefer to accumulate in interfaces but at different interfacial depths, rendering a double-layer ionic distribution within ∼1 nm near the Gibbs dividing surface. The H3O+ preferentially resides in the topmost layer of the interface, but the OH-, which is enriched in the deeper interfacial layer, has a higher equilibrium concentration due to the more negative free energy of interfacial stabilization [-0.90 (OH-) vs -0.56 (H3O+) kcal/mol]. The present finding of the ionic double-layer distribution may qualitatively offer a self-consistent explanation for the long-term controversy about the acid-base nature of the air-water interface.

Hydroxide and Hydronium Ions Modulate the Dynamic Evolution of Nitrogen Nanobubbles in Water

It has been widely recognized that the pH environment influences the nanobubble dynamics and hydroxide ions adsorbed on the surface may be responsible for the long-term survival of the nanobubbles. However, understanding the distribution of hydronium and hydroxide ions in the vicinity of a bulk nanobubble surface at a microscopic scale and the consequent impact of these ions on the nanobubble behavior remains a challenging endeavor. In this study, we carried out deep potential molecular dynamics simulations to explore the behavior of a nitrogen nanobubble under neutral, acidic, and alkaline conditions and the inherent mechanism, and we also conducted a theoretical thermodynamic and dynamic analysis to address constraints related to simulation duration. Our simulations and theoretical analyses demonstrate a trend of nanobubble dissolution similar to that observed experimentally, emphasizing the limited dissolution of bulk nanobubbles in alkaline conditions, where hydroxide ions tend to reside slightly farther from the nanobubble surface than hydronium ions, forming more stable hydrogen bond networks that shield the nanobubble from dissolution. In acidic conditions, the hydronium ions preferentially accumulating at the nanobubble surface in an orderly manner drive nanobubble dissolution to increase the entropy of the system, and the dissolved nitrogen molecules further strengthen the hydrogen bond networks of systems by providing a hydrophobic environment for hydronium ions, suggesting both entropy and enthalpy effects contribute to the instability of nanobubbles under acidic conditions. These results offer fresh insights into the double-layer distribution of hydroxide and hydronium near the nitrogen-water interface that influences the dynamic behavior of bulk nanobubbles.

Impact of interfacial curvature on molecular properties of aqueous interfaces

The curvature of soft interfaces plays a crucial role in determining their mechanical and thermodynamic properties, both at macroscopic and microscopic scales. In the case of air/water interfaces, particular attention has recently focused on water microdroplets, due to their distinctive chemical reactivity. However, the specific impact of curvature on the molecular properties of interfacial water and interfacial reactivity has so far remained elusive. Here, we use molecular dynamics simulations to determine the effect of curvature on a broad range of structural, dynamical, and thermodynamical properties of the interface. For a droplet, a flat interface, and a cavity, we successively examine the structure of the hydrogen-bond network and its relation to vibrational spectroscopy, the dynamics of water translation, rotation, and hydrogen-bond exchanges, and the thermodynamics of ion solvation and ion-pair dissociation. Our simulations show that curvature predominantly impacts the hydrogen-bond structure through the fraction of dangling OH groups and the dynamics of interfacial water molecules. In contrast, curvature has a limited effect on solvation and ion-pair dissociation thermodynamics. For water microdroplets, this suggests that the curvature alone cannot fully account for the distinctive reactivity measured in these systems, which are of great importance for catalysis and atmospheric chemistry.

Prebiotic chemical reactivity in solution with quantum accuracy and microsecond sampling using neural network potentials

While RNA appears as a good candidate for the first autocatalytic systems preceding the emergence of modern life, the synthesis of RNA oligonucleotides without enzymes remains challenging. Because the uncatalyzed reaction is extremely slow, experimental studies bring limited and indirect information on the reaction mechanism, the nature of which remains debated. Here, we develop neural network potentials (NNPs) to study the phosphoester bond formation in water. While NNPs are becoming routinely applied to nonreactive systems or simple reactions, we demonstrate how they can systematically be trained to explore the reaction phase space for complex reactions involving several proton transfers and exchanges of heavy atoms. We then propagate at moderate computational cost hundreds of nanoseconds of a variety of enhanced sampling simulations with quantum accuracy in explicit solvent conditions. The thermodynamically preferred reaction pathway is a concerted, dissociative mechanism, with the transient formation of a metaphosphate transition state and direct participation of water solvent molecules that facilitate the exchange of protons through the nonbridging phosphate oxygens. Associative-dissociative pathways, characterized by a much tighter pentacoordinated phosphate, are higher in free energy. Our simulations also suggest that diprotonated phosphate, whose reactivity is never directly assessed in the experiments, is significantly less reactive than the monoprotonated species, suggesting that it is probably never the reactive species in normal pH conditions. These observations rationalize unexplained experimental results and the temperature dependence of the reaction rate, and they pave the way for the design of more efficient abiotic catalysts and activating groups.

Tuning Acid-Base Chemistry at an Electrified Gold/Water Interface

Acid-base reactions are ubiquitous in solution chemistry, as well as in electrochemistry. However, macroscopic concepts derived in solutions, such as pKa and pH, differ significantly at electrified metal-aqueous interfaces due to specific solvation and applied voltage. Here, we measure the pKa values of an amino acid, glycine, at a gold/water interface under a varying applied voltage by means of spectroscopic titration. With the help of simulations, we propose a general model to understand potential-dependent shifts in pKa values in terms of local hydrophobicity and electric fields. These parameters can be tuned by adjusting the metal surface and applied voltage, respectively, offering promising, but still unexplored, paths to regulate reactivity. Our results change the focus with respect to common interpretations based on, for example, apparent local pH effects and open interesting perspectives for electrochemical reaction steering.

Determining the mechanical and decomposition properties of high energetic materials (α-RDX, β-HMX, and ε-CL-20) using a neural network potential

Molecular simulations of high energetic materials (HEMs) are limited by efficiency and accuracy. Recently, neural network potential (NNP) models have achieved molecular simulations of millions of atoms while maintaining the accuracy of density functional theory (DFT) levels. Herein, an NNP model covering typical HEMs containing C, H, N, and O elements is developed. The mechanical and decomposition properties of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), hexahydro-1,3,5-trinitro-1,3,5-triazine (HMX), and 2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) are determined by employing the molecular dynamics (MD) simulations based on the NNP model. The calculated results show that the mechanical properties of α-RDX, β-HMX, and ε-CL-20 agree with previous experiments and theoretical results, including cell parameters, equations of state, and elastic constants. In the thermal decomposition simulations, it is also found that the initial decomposition reactions of the three crystals are N-NO2 homolysis, corresponding radical intermediates formation, and NO2-induced reactions. This decomposition trajectory is mainly divided into two stages separating from the peak of NO2: pyrolysis and oxidation. Overall, the NNP model for C/H/N/O elements in this work is an alternative reactive force field for RDX, HMX, and CL-20 HEMs, and it opens up new potential for future kinetic study of nitramine explosives.

Neural Network-Based Sum-Frequency Generation Spectra of Pure and Acidified Water Interfaces with Air

The affinity of hydronium ions (H3O+) for the air-water interface is a crucial question in environmental chemistry. While sum-frequency generation (SFG) spectroscopy has been instrumental in indicating the preference of H3O+ for the interface, key questions persist regarding the molecular origin of the SFG spectral changes in acidified water. Here we combine nanosecond long neural network (NN) reactive simulations of pure and acidified water slabs with NN predictions of molecular dipoles and polarizabilities to calculate SFG spectra of long reactive trajectories including proton transfer events. Our simulations show that H3O+ ions cause two distinct changes in phase-resolved SFG spectra: first, a low-frequency tail due to the vibrations of H3O+ and its first hydration shell, analogous to the bulk proton continuum, and second, an enhanced hydrogen-bonded band due to the ion-induced static field polarizing molecules in deeper layers. Our calculations confirm that changes in the SFG spectra of acidic solutions are caused by hydronium ions preferentially residing at the interface.

Ab Initio Driven Exploration on the Thermal Properties of Al-Li Alloy

Al-Li alloys are feasible and promising additives in advanced energy and propellant systems due to the significantly enhanced heat release and increased specific impulse. The thermal properties of Al-Li alloys directly determine the manufacturing, storage safety, and ignition delay of propellants. In this study, a neural network potential (NNP) is developed to investigate the thermal behaviors of Al-Li alloys from an atomistic perspective. The novel NNP demonstrates an excellent predictive ability for energy, atomic force, mechanical behaviors, phonon vibrations, and dynamic evolutions. A series of NNP-based molecular dynamics simulations are performed to investigate the effect of Li doping on the thermal properties of Al-Li alloys. All calculated results for Al-Li alloys are consistent with experimental values for Al, ensuring their validity in predicting Al-Li interactions. The simulation results suggest that a minor increment in the Li content results in a slight change in the melting point, thermal expansion, and radical distribution functions. These three properties are associated with the lattice characteristics; nonetheless, it causes a substantial reduction in thermal conductivity, which is related to the physical properties of the elements. The lower thermal conductivity allows heat accumulation on the particle surface, thereby speeding up the surface premelt and ignition. This provides an alternative atomic explanation for the improved combustion performance of Al-Li alloys. These findings integrate insights from the field of alloy material science into crucial combustion applications, serving as an atomistic guide for developing manufacturing techniques.

Interfacial chemical reactivity enhancement

Interfacial enhancements of chemical reaction equilibria and rates in liquid droplets are predicted using a combined theoretical and experimental analysis strategy. Self-consistent solutions of reaction and adsorption equilibria indicate that interfacial reactivity enhancement is driven primarily by the adsorption free energy of the product (or activated complex). Reactant surface activity has a smaller indirect influence on reactivity due to compensating reactant interfacial concentration and adsorption free energy changes, as well as adsorption-induced depletion of the droplet core. Experimental air-water interfacial adsorption free energies and critical micelle concentration correlations provide quantitative surface activity estimates as a function of molecular structure, predicting an increase in interfacial reactivity with increasing product size and decreasing product polarity, aromaticity, and charge (but less so for anions than cations). Reactions with small, neutral, or charged products are predicted to have little reactivity enhancement at an air-water interface unless the product is rendered sufficiently surface active by, for example, interactions with interfacial water dangling OH groups, charge transfer, or voltage fluctuations.

Surfaces of Atmospheric Droplet Models Probed with Synchrotron XPS on a Liquid Microjet

ConspectusThe atmosphere is a key part of the earth system comprising myriad chemical species in all basic forms of matter. Ubiquitous nano- and microscopic aerosol particles and cloud droplets suspended in the air play crucial roles in earth's climate and the formation of air pollution. Surfaces are a prominent part of aerosols and droplets, due to the high surface area to bulk volume ratios, but very little is known about their specific properties. Many atmospheric compounds are surface-active, leading to enhanced surface concentrations in aqueous solutions. Their distribution between the surface and bulk may determine heterogeneous chemistry and many other properties of aerosol and cloud droplets, but has not been directly observed.We used X-ray photoelectron spectroscopy (XPS) to obtain direct molecular-level information on the surface composition and structure of aqueous solutions of surface-active organics as model systems for atmospheric aerosol and cloud droplets. XPS is a vacuum-based technique enabled for volatile aqueous organic samples by the application of a high-speed liquid microjet. In combination with brilliant synchrotron X-rays, the chemical specificity of XPS allows distinction between elements in different chemical states and positions within molecular structures. We used core-level C 1s and N 1s signals to identify the alkyl and hydrophilic groups of atmospheric carboxylic acids, alkyl-amines, and their conjugate acids and bases. From this, we infer changes in the orientation of surface-adsorbed species and quantify their relative abundances in the surface. XPS-derived surface enrichments of the organics follow trends expected from their surface activities and we observed a preferential orientation at the surface with the hydrophobic alkyl chains pointing increasingly outward from the solution at higher concentrations. This provides a first direct experimental observation of well-established concepts of surface adsorption and confirms the soundness of the method.We mapped relative abundances of conjugate acid-base pairs in the aqueous solution surfaces from the respective intensities of distinctive XPS signals. For each pair, the protonation equilibrium was significantly shifted toward the neutral form in the surface, compared to the bulk solution, across the full pH range. This represents an apparent shift of the pKa in the surface, which may be toward either higher or lower pH, depending on whether the acid or base form of the pair is the neutral species. The surface shifts are broadly consistent with the relative differences in surface enrichment of the individual acid and base conjugates in binary aqueous solutions, with additional contributions from nonideal interactions in the surface. In aqueous mixtures of surface-active carboxylate anions with ammonium salts at near-neutral pH, we found that the conjugate carboxylic acids were further strongly enhanced. This occurs as the coadsorption of weakly basic carboxylate anions and weakly acidic ammonium cations forms ion-pair surface layers with strongly enhanced local abundances, increasing the probability of net proton transfer according to Le Chatelier's principle. The effect is stronger when the evaporation of ammonia from the surface further contributes to irreversibly perturb the protonation equilibrium, leaving a surplus of carboxylic acid. These surface-specific effects may profoundly influence atmospheric chemistry mediated by aqueous aerosols and cloud droplets but are currently not taken into account in atmospheric models.

How the Acidity of Water Droplets and Films Is Controlled by the Air-Water Interface

Acidity is a key determinant of chemical reactivity in atmospheric aqueous aerosols and water microdroplets used for catalysis. However, many fundamental questions about these systems have remained elusive, including how their acidity differs from that of bulk solutions, the degree of heterogeneity between their core and surface, and how the acid-base properties are affected by their size. Here, we perform hybrid density functional theory (DFT)-quality neural network-based molecular simulations with explicit nuclear quantum effects and combine them with an analytic model to describe the pH and self-ion concentrations of droplets and films for sizes ranging from nm to μm. We determine how the acidity of water droplets and thin films is controlled by the properties of the air-water interface and by their surface-to-volume ratio. We show that while the pH is uniform in each system, hydronium and hydroxide ions exhibit concentration gradients that span the two outermost molecular layers, enriching the interface with hydronium cations and depleting it with hydroxide anions. Acidity depends strongly on the surface-to-volume ratio for system sizes below a few tens of nanometers, where the core becomes enriched in hydroxide ions and the pH increases as a result of hydronium stabilization at the interface. These results obtained for pure water systems have important implications for our understanding of chemical reactivity in atmospheric aerosols and for catalysis in aqueous microdroplets.

Accurate Computation of Aqueous pKas of Biologically Relevant Organic Acids: Overcoming the Challenges Posed by Multiple Conformers, Tautomeric Equilibria, and Disparate Functional Groups with the Fully Black-Box pK-Yay Method

The use of static electronic structure calculations to compute solution-phase pKas offers a great advantage in that a macroscopic bulk property could be computed via microscopic computations involving very few molecules. There are various sources of errors in the quantum chemical calculations though. Overcoming these errors to accurately compute pKas of a plethora of acids is an active area of research in physical chemistry pursued by both computational as well as experimental chemists. We recently developed the pK-Yay method in our attempt to accurately compute aqueous pKas of strong and weak acids. The method is fully black-box, computationally inexpensive, and is very easy for even a nonexpert to use. However, the method was thus far tested on very few molecules (only 16 in all). Herein, in order to assess the future applicability of pK-Yay, we study the effect of multiple conformers, the presence of tautomers under equilibrium, and the impact of a wide variety of functional groups (derivatives of acetic acid with substituents at various positions, dicarboxylic acids, aromatic carboxylic acids, amines and amides, phenols and thiols, and fluorine bearing organic acids). Starting with more than 1000 conformers and tautomers, this study establishes that overall errors of ∼ 1.0 pKa units are routinely obtained for a majority of the molecules. Larger errors are noted in cases where multiple charges, intramolecular hydrogen bonding, and several ionizable functional groups are simultaneously present. An important conclusion to emerge from this work is that, the computed pKas are insensitive (difference <0.5) to whether we consider multiple conformers/tautomers or only choose the most stable conformer/tautomer. Further, pK-Yay captures the stereoelectronic effects arising due to differing axial vs equatorial pattern, and is useful to predict the dominant acid-base equilibrium in a system featuring several equilibria. Overall, pK-Yay may be employed in several chemical applications featuring organic molecules and biomonomers.

Speciation of La3+-Cl- Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics

The stability of rare earth element (REE) complexes plays a crucial role in quantitatively assessing their hydrothermal migration and transformation. However, reliable data are lacking under high-temperature hydrothermal conditions, which hampers our understanding of the association behavior of REE. Here a deep learning potential model for the LaCl3-H2O system in hydrothermal fluids is developed based on the first-principles density functional theory calculations. The model accurately predicts the radial distribution functions compared to ab initio molecular dynamics (AIMD) simulations. Furthermore, species of La-Cl complexes, the dissociation pathway of the La-Cl complexes dissociation process, and the potential of mean forces and corresponding association constants (logK) for LaCln3-n (n = 1-4) are extensively investigated under a wide range of temperatures and pressures. Empirical density models for logK calculation are fitted with these data and can accurately predict logK data from both experimental results and AIMD simulations. The distribution of La-Cl species is also evaluated across a wide range of temperatures, pressures, and initial chloride concentration conditions. The results show that La-Cl complexes are prone to forming in a low-density solution, and the number of bonded Cl- ions increases with rising temperature. In contrast, in a high-density solution, La3+ dominates and becomes the more prevalent species.

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

Microscopic Mechanism of Proton Transfer in Pure Water under Ambient Conditions

Water molecules and the associated proton transfer (PT) are prevalent in chemical and biological systems and have been a hot research topic. Spectroscopic characterization and ab initio molecular dynamics (AIMD) simulations have previously revealed insights into acidic and basic liquids. Presumably, the situation in the acidic/basic solution is not necessarily the same as in pure water; in addition, the autoionization constant for water is only 10^-14 under ambient conditions, making the study of PT in pure water challenging. To overcome this issue, we modeled periodic water box systems containing 1000 molecules for tens of nanoseconds based on a neural network potential (NNP) with quantum mechanical accuracy. The NNP was generated by training a dataset containing the energies and atomic forces of 17 075 configurations of periodic water box systems, and these data points were calculated at the MP2 level that considers electron correlation effects. We found that the size of the system and the duration of the simulation have a significant impact on the convergence of the results. With these factors considered, our simulations showed that hydronium (H₃O⁺) and hydroxide (OH^-) ions in water have distinct hydration structures, thermodynamic and kinetic properties, e.g., the longer-lasting and more stable hydrated structure of OH^- ions than that of H₃O⁺, as well as a significantly higher free energy barrier for the OH^--associated PT than that of H₃O⁺, leading the two to exhibit completely different PT behaviors. Given these characteristics, we further found that PT via OH^- ions tends not to occur multiple times or between many molecules. In contrast, PT via H₃O⁺ can synergistically occur among multiple molecules and prefers to adopt a cyclic pattern among three water molecules, while it occurs mostly in a chain pattern when more water molecules are involved. Therefore, our studies provide a detailed and solid microscopic explanation for the PT process in pure water.

Direct quantification of changes in pH within single levitated microdroplets and the kinetics of nitrate and chloride depletion

The hygroscopicity and pH of aqueous microdroplets and smaller aerosols control their impacts on human health and the climate. Nitrate depletion and chloride depletion through the partitioning of HNO₃ and HCl into the gas phase are processes that are enhanced in micron-sized and smaller aqueous droplets and this depletion influences both hygroscopicity and pH. Despite a number of studies, uncertainties remain about these processes. While acid evaporation and the loss of HCl or HNO₃ have been observed during dehydration, there is a question as to the rate of acid evaporation and whether this can occur in fully hydrated droplets at higher relative humidity (RH). To directly elucidate the kinetics of nitrate and chloride depletion through evaporation of HNO₃ and HCl, respectively at high RH, single levitated microdroplets are probed with cavity-enhanced Raman spectroscopy. Using glycine as a novel in situ pH probe, we are able to simultaneously measure changes in microdroplet composition and pH over timescales of hours. We find that the loss of chloride from the microdroplet is faster than that of nitrate, and the calculated rate constants infer that depletion is limited by the formation of HCl or HNO₃ at the air-water interface and subsequent partitioning into the gas phase.

Monitoring the melting behavior of boron nanoparticles using a neural network potential

The melting behavior of metal additives is fundamental for various propulsion and energy-conversion applications. A neural network potential (NNP) is proposed to examine the size-dependent melting behaviors of boron nanoparticles. Our NNP model is proven to possess a desirable computational efficiency and retain ab initio accuracy, allowing investigation of the physicochemical properties of bulk boron crystals from an atomic perspective. In this work, a series of NNP-based molecular dynamics simulations were conducted and numerical evidence of the size-dependent melting behavior of boron nanoparticles with diameters from 3 to 6 nm was reported for the first time. Evolution of the intermolecular energy and the Lindemann index are used to monitor the melting process. A liquid layer forms on the particle surface and further expands with increased temperature. Once the liquid layer reaches the core region, the particle is completely molten. The reduced melting temperature of the boron nanoparticle decreases with its particle size following a linear relationship with reciprocal size, similar to other commonly used metals (Al and Mg). Additionally, boron nanoparticles are more sensitive to particle size than Al particles and less sensitive than Mg particles. These findings provide an atomistic perspective for developing manufacturing techniques and tailoring combustion performance in practical applications.

Counterintuitive Oxidation of Alcohols at Air-Water Interfaces

This study shows that the oxidation of alcohols can rapidly occur at air-water interfaces. It was found that methanediols (HOCH₂OH) orient at air-water interfaces with a H atom of the -CH₂- group pointing toward the gaseous phase. Counterintuitively, gaseous hydroxyl radicals do not prefer to attack the exposed -CH₂- group but the -OH group that forms hydrogen bonds with water molecules at the surface via a water-promoted mechanism, leading to the formation of formic acids. Compared with gaseous oxidation, the water-promoted mechanism at the air-water interface significantly lowers free-energy barriers from ∼10.7 to ∼4.3 kcal·mol^-1 and therefore accelerates the formation of formic acids. The study unveils a previously overlooked source of environmental organic acids that are bound up with aerosol formation and water acidity.

Mechanistic Insights into the Reactive Uptake of Chlorine Nitrate at the Air-Water Interface

It is well-known that the aqueous-phase processing of chlorine nitrate (ClONO₂) plays a crucial role in ozone depletion. However, many of the physical and chemical properties of ClONO₂ at the air-water interface or in bulk water are unknown or not understood on a microscopic scale. Here, the solvation and hydrolysis of ClONO₂ at the air-water interface and in bulk water at 300 K were investigated by classical and ab initio molecular dynamics (AIMD) simulations combined with free energy methods. Our results revealed that ClONO₂ prefers to accumulate at the air-water interface rather than in the bulk phase. Specifically, halogen bonding interactions (ClONO₂)Cl···O(H₂O) were found to be the predominant interactions between ClONO₂ and H₂O. Moreover, metadynamics-biased AIMD simulations revealed that ClONO₂ hydrolysis is catalyzed at the air-water interface with an activation barrier of only ∼0.2 kcal/mol; additionally, the difference in free energy between the product and reactant is only ∼0.1 kcal/mol. Surprisingly, the near-barrierless reaction and the comparable free energies of the reactant and product suggested that the ClONO₂ hydrolysis at the air-water interface is reversible. When the temperature is lowered from 300 to 200 K, the activation barrier for the ClONO₂ hydrolysis at the air-water interface is increased to ∼5.4 kcal/mol. These findings have important implications for the interpretation of experiments.

Reaction dynamics of Diels-Alder reactions from machine learned potentials

Recent advances in the development of reactive machine-learned potentials (MLPs) promise to transform reaction modelling. However, such methods have remained computationally expensive and limited to experts. Here, we employ different MLP methods (ACE, NequIP, GAP), combined with automated fitting and active learning, to study the reaction dynamics of representative Diels-Alder reactions. We demonstrate that the ACE and NequIP MLPs can consistently achieve chemical accuracy (±1 kcal mol^-1) to the ground-truth surface with only a few hundred reference calculations. These strategies are shown to enable routine ab initio-quality classical and quantum dynamics, and obtain dynamical quantities such as product ratios and free energies from non-static methods. For ambimodal reactions, product distributions were found to be strongly dependent on the QM method and less so on the type of dynamics propagated.

Sum-frequency vibrational spectroscopy of methanol at interfaces due to Fermi resonance

We present a theoretical method of studying sum-frequency vibrational spectroscopy for the CH3 group of methanol at interfaces due to Fermi resonance, which provides a novel and untraditional point of view with respect to traditional approaches.