Hiding in the Crowd: Spectral Signatures of Overcoordinated Hydrogen-Bond Environments

Investigating the Spectroscopy of the Gas Phase Guanine-Cytosine Pair: Keto versus Enol Configurations

We report on a vibrational study of the guanine-cytosine dimer tautomers using state-of-the-art quasiclassical trajectory and semiclassical vibrational spectroscopy. The latter includes possible quantum mechanical effects. Through an accurate comparison to the experimental spectra, we are able to shine a light on the hydrogen bond network of one of the main subunits of DNA and put the experimental assignment on a solid footing. Our calculations corroborate the experimental conclusion that the global minimum Watson-and-Crick structure is not detected in the spectra, and there is no evidence of tunnel-effect-based double proton hopping. Our accurate assignment of the spectral features may also serve as a basis for the development of precise force fields to study the guanine-cytosine dimer.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS): Part III. Versatile applications

In this review, the comprehensive summary of two-dimensional correlation spectroscopy (2D-COS) for the last two years is covered. The remarkable applications of 2D-COS in diverse fields using many types of probes and perturbations for the last two years are highlighted. IR spectroscopy is still the most popular probe in 2D-COS during the last two years. Applications in fluorescence and Raman spectroscopy are also very popularly used. In the external perturbations applied in 2D-COS, variations in concentration, pH, and relative compositions are dramatically increased during the last two years. Temperature is still the most used effect, but it is slightly decreased compared to two years ago. 2D-COS has been applied to diverse systems, such as environments, natural products, polymers, food, proteins and peptides, solutions, mixtures, nano materials, pharmaceuticals, and others. Especially, biological and environmental applications have significantly emerged. This survey review paper shows that 2D-COS is an actively evolving and expanding field.

Hydrogen-Bond Configurations of Hydration Water around Glycerol Investigated by HOH Bending and OH Stretching Analysis

Toward a comprehensive understanding of the mechanism of glycerol as a moisturizer, studies on the hydrogen-bond (HB) structure of hydration water, which is known to be disordered by glycerol, are insufficient. To this aim, we evaluated the HB configurations based on the HOH bending and OH stretching spectra of the hydration water from those of glycerol/water mixtures by subtracting the contributions of bulk water and glycerol using dielectric relaxation spectroscopy. Analysis of the HOH bending band showed that hydration water-donating HBs lose the intermolecular bending coupling with increasing glycerol by replacing the water-water HBs with water-glycerol HBs. The OH stretching band provided more detailed insight into the HB configuration, indicating that the double-donor double-acceptor and double-donor single-acceptor configurations in bulk water change to a predominantly double-donor single-acceptor configuration in hydration water around glycerol. The formation of more donor HBs than acceptor HBs may be due to the steric constrains by glycerol and/or differences in the partial charge on the oxygen atom between water and glycerol.

Correlated Local Fluctuations in the Hydrogen Bond Network of Liquid Water

The hypothesis that liquid water can separate into two phases in the supercooled state has been supported by recent experimental and theoretical studies. However, whether such structural inhomogeneity extends to ambient conditions is under intense debate. Due to the dynamic nature of the hydrogen bond network of liquid water, exploring its structure requires detailed insight into the collective motion of neighboring water molecules, a missing link that has not been examined so far. Here, highly sensitive quantum mechanical calculations detect that the time evolution of nearby hydrogen bonds is strongly correlated, revealing a direct mechanism for the appearance of short-range structural fluctuations in the hydrogen bond network of liquid water for the first time. This correlated dynamics is found to be closely connected to the static structural picture. The distortions from the tetrahedral structure do not occur independently but are correlated due to the preference of nearby donors and acceptors to be in similar environments. The existence of such cooperative fluctuations is further supported by the temperature dependence of the local structural evolution and explained by conventional analysis of localized orbitals. It was found that such correlated structural fluctuations are only observed on a short length scale in simulations at ambient conditions. The correlations of the nearby hydrogen bond pairs of liquid water unveiled here are expected to offer a new insight into connecting the dynamics of individual water molecules and the local structure of the hydrogen bond network.

Influence of Methylene Fluorination and Chain Length on the Hydration Shell Structure and Thermodynamics of Linear Diols

The interplay between the local hydration shell structure, the length of hydrophobic solutes, and their identity (perfluorinated or not) remains poorly understood. We address this issue by combining Raman-multivariate curve resolution (Raman-MCR) spectroscopy, simulation, and quantum-mechanical calculations to quantify the thermodynamics and the first principle interactions behind the formation of defects in the hydration shell of alkyl-diol and perfluoroalkyl-diol chains. The hydration shell of the fluorinated diols contains substantially more defects than that of the nonfluorinated diols; these defects are water hydroxy groups that do not donate hydrogen bonds and which either point to the solute (radial-dangling OH) or not (nonradial-dangling OH). The number of radial-dangling OH defects per carbon decreases for longer chains and toward the interior of the fluorinated diols, mainly due to less favorable electrostatics and exchange interactions; nonradial-dangling OH defects per carbon increase with chain length. In contrast, the hydration shell of the nonfluorinated diols only contains radial-dangling defects, which become more abundant toward the center of the chain and for larger chains, predominantly because of more favorable dispersion interactions. These results have implications for how the folding of macromolecules, ligand binding to biomacromolecules, and chemical reactions at water-oil interfaces could be modified through the introduction of fluorinated groups or solvents.

Freezing of Dilute Aqueous-Alcohol Nanodroplets: The Effect of Molecular Structure

We investigate vapor-liquid nucleation and subsequent freezing of aqueous-alcohol nanodroplets containing 1-pentanol, 1-hexanol, and their 3-isomers. The aerosols are produced in a supersonic nozzle, where condensation and freezing are characterized by static pressure and Fourier transform Infrared (FTIR) spectroscopy measurements. At fixed water concentrations, the presence of alcohol enables particle formation at higher temperatures since both the equilibrium vapor pressure above the critical clusters and the cluster interfacial free energy are decreased relative to the pure water case. The disappearance of a small free OH peak, observed for pure water droplets, when alcohols are added and shifts in the CH peaks as a function of alcohol chain length reveal varying surface partitioning preferences of the alcohols. Changes in the FTIR spectra during freezing, as well as changes in the ice component derived from self-modeling curve resolution analysis, show that 1-hexanol and 1-pentanol perturb freezing less than their branched isomers do. This behavior may reflect the molecular footprints of the alcohols, the available surface area of the droplets, and not only alcohol solubility. The presence of alcohols also lowers the freezing temperature relative to that of pure water, but when there is clear evidence for the formation of ice, the ice nucleation rates change by less than a factor of ∼2-3 for all cases studied.

AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet-TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed-memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

Eutectics: formation, properties, and applications

Various eutectic systems have been proposed and studied over the past few decades. Most of the studies have focused on three typical types of eutectics: eutectic metals, eutectic salts, and deep eutectic solvents. On the one hand, they are all eutectic systems, and their eutectic principle is the same. On the other hand, they are representative of metals, inorganic salts, and organic substances, respectively. They have applications in almost all fields related to chemistry. Their different but overlapping applications stem from their very different properties. In addition, the proposal of new eutectic systems has greatly boosted the development of cross-field research involving chemistry, materials, engineering, and energy. The goal of this review is to provide a comprehensive overview of these typical eutectics and describe task-specific strategies to address growing demands.

Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations

Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum-mechanics based methods. At the same time, the construction of new machine-learning potentials can seem a daunting task, as it involves data-science techniques that are not yet common in chemistry and materials science. Here, we provide a tutorial-style overview of strategies and best practices for the construction of artificial neural network (ANN) potentials. We illustrate the most important aspects of (a) data collection, (b) model selection, (c) training and validation, and (d) testing and refinement of ANN potentials on the basis of practical examples. Current research in the areas of active learning and delta learning are also discussed in the context of ANN potentials. This tutorial review aims at equipping computational chemists and materials scientists with the required background knowledge for ANN potential construction and application, with the intention to accelerate the adoption of the method, so that it can facilitate exciting research that would otherwise be challenging with conventional strategies.

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

Fourier transform infrared spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using convolutional neural networks (CNNs) to identify the presence of functional groups in gas-phase FTIR spectra. The ML models reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas-phase organic molecules within the NIST spectral database and transform the data into spectral images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that infer in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.

The freezing behavior of aqueous n-alcohol nanodroplets

We generate water-rich aerosols containing 1-propanol and 1-pentanol in a supersonic nozzle to study the effects of these solutes on the freezing behavior of water. Condensation and freezing are characterized by two complementary techniques, pressure trace measurements and Fourier Transform Infrared spectroscopy. When 1-pentanol and 1-propanol are present, condensation occurs at higher temperatures because particle formation from the vapor phase is enhanced by the decrease in interfacial free energy of mixed aqueous-alcohol critical clusters relative to those of pure water. FTIR results suggest that when ∼6 nm radius droplets freeze, the tetrahedral structure of the ice is well preserved up to an overall alcohol mole fraction of 0.031 for 1-propanol and 0.043 for 1-pentanol. In this concentration range, the ice nucleation temperature decreases continuously with increasing 1-propanol concentration, whereas the onset of freezing is not significantly perturbed by 1-pentanol up to a mole fraction of 0.03. Furthermore, once freezing starts the ice nucleation rates in the aqueous-alcohol droplets are very close to those for pure water. In contrast, at the highest mole fractions of either alcohol it is not clear whether droplets freeze to form crystalline ice since the final state of the particles cannot be adequately characterized with the available experimental techniques.

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.

Role of Intermolecular Charge Fluxes in the Hydrogen-Bond-Induced Frequency Shifts of the OH Stretching Mode of Water

The relation between the vibrational properties and the electrostatic situations of the vibrating functional group is useful to predict vibrational spectroscopic features based on, for example, classical molecular dynamics of liquids or biomolecular systems, but to pursue its generality or the extent of applicability, it is required to understand the mechanisms giving rise to it. Here such an analysis is carried out for the OH stretching mode of water. By examining the correlations among various (structural, vibrational, and electrostatic) properties and by analyzing the spatial characteristics of the behavior of electrons occurring upon the vibration, it is shown that the dependence of the vibrational frequency and the dipole derivative of the OH stretching mode on the electric field is not of purely electrostatic origin, and the delocalized electronic motions occurring with this mode, called intermolecular charge fluxes, related to both the dipole first and second derivatives play important roles.

How many water molecules are needed to solvate one?

Many efforts undertaken to study the solvation process have led to general theories that may describe mean properties, but are unable to provide a detailed understanding at the molecular level. Remarkably, the basic question of how many solvent molecules are necessary to solvate one solute molecule is still open. By exploring several water aggregates of increasing complexity, in this contribution we employ semiclassical spectroscopy to determine on quantum dynamical grounds the minimal network of surrounding water molecules to make the central one display the same vibrational features of liquid water. We find out that double-acceptor double-donor tetrahedral coordination constituting the standard picture is necessary but not sufficient, and that particular care must be reserved for the quantum description of the combination band due to the coupling of the central monomer bending mode with network librations. It is actually our ability to investigate the combination band with a quantum-derived approach that allows us to answer the titular question. The minimal structure eventually responsible for proper solvation is made of a total of 21 water molecules and includes two complete solvation shells, of which the whole first one is tetrahedrally coordinated to the central molecule.

How quantum spectroscopic simulations can explain water solvation by comparison with experimental spectra.

Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments

The excited-state dynamics of chromophores in complex environments determine a range of vital biological and energy capture processes. Time-resolved, multidimensional optical spectroscopies provide a key tool to investigate these processes. Although theory has the potential to decode these spectra in terms of the electronic and atomistic dynamics, the need for large numbers of excited-state electronic structure calculations severely limits first-principles predictions of multidimensional optical spectra for chromophores in the condensed phase. Here, we leverage the locality of chromophore excitations to develop machine learning models to predict the excited-state energy gap of chromophores in complex environments for efficiently constructing linear and multidimensional optical spectra. By analyzing the performance of these models, which span a hierarchy of physical approximations, across a range of chromophore-environment interaction strengths, we provide strategies for the construction of machine learning models that greatly accelerate the calculation of multidimensional optical spectra from first principles.

Insights into the Emerging Networks of Voids in Simulated Supercooled Water

The structural evolution of supercooled liquid water as we approach the glass transition temperature continues to be an active area of research. Here, we use molecular dynamics simulations of TIP4P/ice water to study the changes in the connected regions of empty space within the liquid, which we investigate using the Voronoi-voids network. We observe two important features: supercooling enhances the fraction of nonspherical voids and different sizes of voids tend to cluster forming a percolating network. By examining order parameters such as the local structure index (LSI), tetrahedrality and topological defects, we show that water molecules near large void clusters tend to be slightly more tetrahedral than those near small voids, with a lower population of under- and overcoordinated defects. We show further that the distribution of closed rings of water molecules around small and large void clusters maintain a balance between 6 and 7 membered rings. Our results highlight the changes of the dual voids and water network as a structural hallmark of supercooling and provide insights into the molecular origins of cooperative effects underlying density fluctuations on the subnanometer and nanometer length scale. In addition, the percolation of the voids and the hydrogen bond network around the voids may serve as useful order parameters to investigate density fluctuations in supercooled water.

Hydration interactions beyond the first solvation shell in aqueous phenolate solution

We investigate the orientational dynamics of water molecules solvating phenolate ions using ultrafast vibrational spectroscopy and density functional theory-based molecular dynamics simulations.