Infrared and Raman Spectroscopy of Liquid Water through "First-Principles" Many-Body Molecular Dynamics

Terahertz calorimetry spotlights the role of water in biological processes

Terahertz (THz) calorimetry is a framework that allows for the deduction and quantification of changes in solvation entropy and enthalpy associated with biological processes in real-time. Fundamental biological processes are inherently non-equilibrium, and a small imbalance in free energy can trigger protein condensation or folding. Although biophysical techniques typically focus mainly on structural characterization, water is often ignored. Being a generic solvent, the intermolecular protein-water interactions act as a strong competitor for intramolecular protein-protein interactions, leading to a delicate balance between functional structure formation and complete solvation. Characteristics for biological processes are large, but competing enthalpic and entropic solvation contributions to the total Gibbs free energy lead to subtle energy differences of only a few kJ mol-1 that are capable of dictating biological functions. THz calorimetry spotlights these intermolecular coupled protein-water interactions. With experimental advances in THz technology, a new frequency window has opened, which is ideally suited to probe these low-frequency intermolecular interactions. The future impact of these studies is based on the belief that the observed changes in solvation entropy and enthalpy are not secondary effects but dictate biological function.

Analytical Derivatives of Symmetry-Adapted Perturbation Theory Corrections for Interaction-Induced Properties

A new approach that allows for the calculation of interaction-induced properties exclusively from the properties of monomers is presented. The method is derived in the spirit of the symmetry-adapted perturbation theory (SAPT). The interaction-induced property is presented in the first order of the molecular interaction operator, including the exchange effects. Test calculations of the interaction-induced dipole moment were carried out for a number of small nonpolar and polar atomic and molecular dimers. The numerical results show that the analytical first-order corrections proposed in this paper reproduce the finite-field treatment of the first-order corrections of SAPT. Compared to supermolecular approaches, the performance of the finite-field SAPT (up to the second order) constitutes an insightful alternative for calculations of interaction-induced properties.

Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential

We present an extended Lagrangian shadow molecular dynamics scheme with an interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize the charge equilibration model, we use machine learning with neural networks to determine the environment-dependent electronegativities and chemical hardness parameters for each atom, in addition to the charge-independent energy and force terms. The approximate shadow molecular dynamics potential in combination with the extended Lagrangian formulation improves the numerical stability and reduces the number of Coulomb potential calculations required to evaluate accurate conservative forces. We demonstrate efficient and accurate simulations with excellent long-term stability of the molecular dynamics trajectories. The significance of choosing fixed or environment-dependent electronegativities and chemical hardness parameters is evaluated. Finally, we compute the infrared spectrum of molecules via the dipole autocorrelation function and compare to experiments to highlight the accuracy of the shadow molecular dynamics scheme with a machine learned flexible charge potential.

Oil-in-water Pickering emulsions with Buriti vegetable oil stabilized with cellulose nanofibrils: Preparation, stability and antimicrobial properties

Mauritia flexuosa (Buriti) vegetable oil (OV) has attracted technological interest in various sectors, including pharmaceuticals, food, and beverages, because of its excellent antioxidant activity. The active OV components are fatty compounds, and stability is required for proper application. In this work, we investigated OV-in-water Pickering emulsions stabilized by cellulose nanofibrils (CNF). CNF is sustainable, economically viable, and environmentally friendly, and it is suitable for developing products in an eco-friendly way. The factorial design of experiments (DoE) indicates that the amount of CNF and the homogenization time significantly affect the emulsion, preventing coalescence over 30 days. Fourier-transform Raman spectroscopy (FT-Raman) and Fourier-transform infrared spectroscopy (FTIR) show that CNF stabilizes the OV droplets through induced dipole-dipole interactions and hydrogen bonds. Rheological analysis was relevant to the relationship between internal microstructure strength and viscous flow behavior of the emulsions. A novel approach enabled the identification of the CNF stabilization mechanism in the emulsion system via fluorescence microscopy. Diameter distribution measurements and steady-state rheological tests indicate that the emulsions have good stability at room temperature and suitable steady-state viscosity for food applications and beverage products as they show pronounced shear thinning behavior for cream and lotion skin care products.

Nuclear Quantum Effects in Neutral Water Clusters at Finite Temperature: Structural Evolution from Two to Three Dimensions

Understanding the structure of bulk water presents a significant challenge due to its intricate hydrogen bond network and dynamic properties. Neutral water clusters, serving as fundamental building blocks, provide key insights into hydrogen bond configurations and intermolecular interactions, thereby establishing a critical foundation for elucidating the behavior of liquid water. In this study, state-of-the-art quantum simulations utilizing a many-body potential for water are employed to investigate the influence of nuclear quantum effects (NQEs) on the structural evolution of neutral water clusters (H2O)n (n = 2-10). For the pentamer at finite temperature, quantum simulations demonstrate that NQEs substantially facilitate the transition from two-dimensional (2D) to three-dimensional (3D) configurations. The population of 3D isomers is governed by a synergistic interplay among the effects of thermal fluctuates and NQEs. For water hexamers with fully 3D structures, the quantum simulations uncover a lower-energy pathway for structural evolution from a prism to a book structure via a cage-like intermediate-a pathway not observed in classical simulations. These findings highlight the crucial role of NQEs in the structural evolution of water clusters and provide a theoretical framework to explore the structure and dynamic properties of condensed-phase water.

A Simple Regeneration Process Using a CO2-Switchable-Polarity Solvent for Cellulose Hydrogels

Cellulose is one of the main components of plant cell walls, abundant on earth, and can be acquired at a low cost. Furthermore, there has been increasing interest in its use in environmentally friendly, carbon-neutral, sustainable materials. It is expected that the applications of cellulose will expand with the development of a simple processing method. In this study, we dissolved cellulose in aqueous N-butyl-N-methylpyrrolidinium hydroxide solution ([C4mpyr][OH]/H2O) and investigated the cellulose regeneration process based on changes in solubility upon application of CO2 gas. We investigated the effect of transformation of the anion chemical structure on cellulose solubility by flowing CO2 gas into [C4mpyr][OH]/H2O and conducted pH, FT-IR, and 13C NMR measurements. We observed that the changes in anion structure allowed for the modulation of cellulose solubility in [C4mpyr][OH]/H2O, thus establishing a simple and safe cellulose regeneration process. This regeneration process was also applied to enable the production of cellulose hydrogels. The hydrogel formed using this method was revealed to have higher mechanical strength than an analogous hydrogel produced using the same dissolution solvent with the addition of a cross-linker. The ability to produce cellulose-based hydrogels of different mechanical properties is expected to expand the possible applications.

Advances and applications of dynamic surface-enhanced Raman spectroscopy (SERS) for single molecule studies

Dynamic surface-enhanced Raman spectroscopy (SERS) is nowadays one of the most interesting applications of SERS, in particular for single molecule studies. In fact, it enables the study of real-time processes at the molecular level. This review summarizes the latest developments in dynamic SERS techniques and their applications, focusing on new instrumentation, data analysis methods, temporal resolution and sensitivity improvements, and novel substrates. We highlight the progress and applications of single-molecule dynamic SERS in monitoring chemical reactions, catalysis, biomolecular interactions, conformational dynamics, and real-time sensing and detection. We aim to provide a comprehensive review on its advancements, applications as well as its current challenges and development frontiers.

Generalized bond polarizability model for more accurate atomistic modeling of Raman spectra

Raman spectroscopy is an important tool for studying molecules, liquids and solids. While Raman spectra can be obtained theoretically from molecular dynamics (MD) simulations, this requires the calculation of electronic polarizability along the simulation trajectory. First-principles calculations of electronic polarizability are computationally expensive, motivating the development of atomistic models for the evaluation of the changes in the electronic polarizability with the changes in the atomic coordinates of the system. The bond polarizability model (BPM) is one of the oldest and simplest such atomistic models but cannot reproduce the effects of angular vibrations, leading to inaccurate modeling of Raman spectra. Here, we demonstrate that the generalization of BPM through the inclusion of terms for atom pairs that are traditionally considered to be not involved in bonding dramatically improves the accuracy of polarizability modeling and Raman spectra calculations. The generalized BPM (GBPM) reproduces the ab initio polarizability and Raman spectra for a range of tested molecules (SO2, H2S, H2O, NH3, CH4, CH3OH, and CH3CH2OH) with high accuracy and also shows significantly improved agreement with ab initio results for the more complex ferroelectric BaTiO3 systems. For liquid water, the anisotropic Raman spectrum derived from atomistic MD simulations using the GBPM evaluation of polarizability shows significantly improved agreement with the experimental spectrum compared to the spectrum derived using the BPM. Thus, the GBPM can be used for the modeling of Raman spectra using large-scale molecular dynamics and provides a good basis for the further development of atomistic polarizability models.

Revealing the Water Structure at Neutral and Charged Graphene/Water Interfaces through Quantum Simulations of Sum Frequency Generation Spectra

The structure and dynamics of water at charged graphene interfaces fundamentally influence molecular responses to electric fields with implications for applications in energy storage, catalysis, and surface chemistry. Leveraging the realism of the MB-pol data-driven many-body potential and advanced path-integral quantum dynamics, we analyze the vibrational sum frequency generation (vSFG) spectrum of graphene/water interfaces under varying surface charges. Our quantum simulations reveal a distinctive dangling OH peak in the vSFG spectrum at neutral graphene, consistent with recent experimental findings yet markedly different from those of earlier studies. As the graphene surface becomes positively charged, interfacial water molecules reorient, decreasing the intensity of the dangling OH peak as the OH groups turn away from the graphene. In contrast, water molecules orient their OH bonds toward negatively charged graphene, leading to a prominent dangling OH peak in the corresponding vSFG spectrum. This charge-induced reorganization generates a diverse range of hydrogen-bonding topologies at the interface driven by variations in the underlying electrostatic interactions. Importantly, these structural changes extend into deeper water layers, creating an unequal distribution of molecules with OH bonds pointing toward and away from the graphene sheet. This imbalance amplifies bulk spectral features, underscoring the complexity of many-body interactions that shape the molecular structure of water at charged graphene interfaces.

Dissecting the Molecular Structure of the Air/Ice Interface from Quantum Simulations of the Sum-Frequency Generation Spectrum

Ice interfaces are pivotal in mediating key chemical and physical processes such as heterogeneous chemical reactions in the environment, ice nucleation, and cloud microphysics. At the ice surface, water molecules form a quasi-liquid layer (QLL) with properties distinct from those of the bulk. Despite numerous experimental and theoretical studies, a molecular-level understanding of the QLL has remained elusive. In this work, we use state-of-the-art quantum dynamics simulations with a realistic data-driven many-body potential to dissect the vibrational sum-frequency generation (vSFG) spectrum of the air/ice interface in terms of contributions arising from individual molecular layers, orientations, and hydrogen-bonding topologies that determine the QLL properties. The agreement between experimental and simulated spectra provides a realistic molecular picture of the evolution of the QLL as a function of the temperature without the need for empirical adjustments. The emergence of specific features in the experimental vSFG spectrum suggests that surface restructuring may occur at lower temperatures. This work not only underscores the critical role of many-body interactions and nuclear quantum effects in understanding ice surfaces but also provides a definitive molecular-level picture of the QLL, which plays a central role in multiphase and heterogeneous processes of relevance to a range of fields, including atmospheric chemistry, cryopreservation, and materials science.

Deep Learning for Generating Phase-Conditioned Infrared Spectra

Infrared (IR) spectroscopy is an efficient method for identifying unknown chemical compounds. To accelerate IR spectrum analysis, various calculation and machine learning methods for simulating IR spectra of molecules have been studied in chemical science. However, existing calculation and machine learning methods assumed a rigid constraint that all molecules are in the gas phase, i.e., they overlooked the phase dependency of the IR spectra. In this paper, we propose an efficient phase-aware machine learning method to generate phase-conditioned IR spectra from 2D molecular structures. To this end, we devised a phase-aware graph neural network and combined it with a transformer decoder. To the best of our knowledge, the proposed method is the first IR spectrum generator that can generate the phase-conditioned IR spectra of real-world complex molecules. The proposed method outperformed state-of-the-art methods in the tasks of generating IR spectra on a benchmark dataset containing experimentally measured 11,546 IR spectra of 10,288 unique molecules. All implementations of the proposed method are publicly available at https://github.com/ngs00/PASGeN.

Current Status of the MB-pol Data-Driven Many-Body Potential for Predictive Simulations of Water Across Different Phases

Developing a molecular-level understanding of the properties of water is central to numerous scientific and technological applications. However, accurately modeling water through computer simulations has been a significant challenge due to the complex nature of the hydrogen-bonding network that water molecules form under different thermodynamic conditions. This complexity has led to over five decades of research and many modeling attempts. The introduction of the MB-pol data-driven many-body potential energy function marked a significant advancement toward a universal molecular model capable of predicting the structural, thermodynamic, dynamical, and spectroscopic properties of water across all phases. By integrating physics-based and data-driven (i.e., machine-learned) components, which correctly capture the delicate balance among different many-body interactions, MB-pol achieves chemical and spectroscopic accuracy, enabling realistic molecular simulations of water, from gas-phase clusters to liquid water and ice. In this review, we present a comprehensive overview of the data-driven many-body formalism adopted by MB-pol, highlight the main results and predictions made from computer simulations with MB-pol to date, and discuss the prospects for future extensions to data-driven many-body potentials of generic and reactive molecular systems.

Modeling Many-Body Interactions in Water with Gaussian Process Regression

We report a first-principles water dimer potential that captures many-body interactions through Gaussian process regression (GPR). Modeling is upgraded from previous work by using a custom kernel function implemented through the KeOps library, allowing for much larger GPR models to be constructed and interfaced with the next-generation machine learning force field FFLUX. A new synthetic water dimer data set, called WD24, is used for model training. The resulting models can predict 90% of dimer geometries within chemical accuracy for a test set and in a simulation. The curvature of the potential energy surface is captured by the models, and a successful geometry optimization is completed with a total energy error of just 2.6 kJ mol-1, from a starting structure where water molecules are separated by nearly 4.3 Å. Dimeric modeling of a flexible, noncrystalline system with FFLUX is shown for the first time.

Five-Site Water Models for Ice and Liquid Water Generated by a Series-Parallel Machine Learning Strategy

Icing, a common natural phenomenon, always originates from a molecule. Molecular simulation is crucial for understanding the relevant process but still faces a great challenge in obtaining a uniform and accurate description of ice and liquid water with limited model parameters. Here, we propose a series-parallel machine learning (ML) approach consisting of a classification back-propagation neural network (BPNN), parallel regression BPNNs, and a genetic algorithm to establish conventional TIP5P-BG and temperature-dependent TIP5P-BGT models. The established water models exhibit a comprehensive balance among the crucial physical properties (melting point, density, vaporization enthalpy, self-diffusion coefficient, and viscosity) with mean absolute percentage errors of 2.65 and 2.40%, respectively, and excellent predictive performance on the related properties of liquid water. For ice, the simulation results on the critical nucleus size and growth rate are in good accordance with experiments. This work offers a powerful molecular model for phase transition and icing in nanoconfinement and a construction strategy for a complex molecular model in the extreme case.

E(n)-Equivariant cartesian tensor message passing interatomic potential

Machine learning potential (MLP) has been a popular topic in recent years for its capability to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However, the information of the node in these models is usually limited to scalars, and vectors. In this work, we propose High-order Tensor message Passing interatomic Potential (HotPP), an E(n) equivariant message passing neural network that extends the node embedding and message to an arbitrary order tensor. By performing some basic equivariant operations, high order tensors can be coupled very simply and thus the model can make direct predictions of high-order tensors such as dipole moments and polarizabilities without any modifications. The tests in several datasets show that HotPP not only achieves high accuracy in predicting target properties, but also successfully performs tasks such as calculating phonon spectra, infrared spectra, and Raman spectra, demonstrating its potential as a tool for future research.

Derivative learning of tensorial quantities-Predicting finite temperature infrared spectra from first principles

We develop a strategy that integrates machine learning and first-principles calculations to achieve technically accurate predictions of infrared spectra. In particular, the methodology allows one to predict infrared spectra for complex systems at finite temperatures. The method's effectiveness is demonstrated in challenging scenarios, such as the analysis of water and the organic-inorganic halide perovskite MAPbI3, where our results consistently align with experimental data. A distinctive feature of the methodology is the incorporation of derivative learning, which proves indispensable for obtaining accurate polarization data in bulk materials and facilitates the training of a machine learning surrogate model of the polarization adapted to rotational and translational symmetries. We achieve polarization prediction accuracies of about 1% for the water dimer by training only on the predicted Born effective charges.

Molecular dynamics simulations reliably identify vibrational modes in far-IR spectra of phospholipids

The properties of self-assembled phospholipid membranes are of essential importance in biochemistry and physical chemistry, providing a platform for many cellular life functions. Far-infrared (far-IR) vibrational spectroscopy, on the other hand, is a highly information-rich method to characterize intermolecular interactions and collective behaviour of lipids that can help explain, e.g., chain packing, thermodynamic phase behaviour, and sequestration. However, reliable interpretation of the far-IR spectra is still lacking. Here we present a molecular dynamics (MD) based approach to simulate vibrational modes of individual lipids and in an ensemble. The results are a good match to synchrotron far-IR measurements and enable identification of the molecular motions corresponding to each vibrational mode, thus allowing the correct interpretation of membrane spectra with high accuracy and resolving the longstanding ambiguities in the literature in this regard. Our results demonstrate the feasibility of using MD simulations for interpreting far-IR spectra broadly, opening new avenues for practical use of this powerful method.

Facile synthesis of Cu-based catalysts from Cu3Si and their catalysis properties study

Supported copper species are well-known for their remarkable catalytic properties across numerous reactions. However, the current preparation methods pose challenges for large-scale production. In this study, we present a cost-effective method for the facile preparation of a series of copper-silicon composites using Cu3Si@Si particles as precursors. We evaluate the catalytic properties of these composites in the conversion of 4-nitrophenol to 4-amionphenol. Notably, the Cu@SiOx/Si composite exhibits exceptional catalytic performance, attributed to the synergy effect between Cu and Si, and the formation of a metastable Si-H2 complex that enhances the reaction kinetics. This research introduces a novel approach for creating efficient and stable catalysts for hydrogenation reactions.

Raman spectroscopy in the study of amyloid formation and phase separation

Neurodegenerative diseases, such as Alzheimer's and Parkinson's, share a common pathological feature of amyloid structure accumulation. However, the structure-function relationship between these well-ordered, β-sheet-rich, filamentous protein deposits and disease etiology remains to be defined. Recently, an emerging hypothesis has linked phase separation, a process involved in the formation of protein condensates, to amyloid formation, suggesting that liquid protein droplets serve as loci for amyloid initiation. To elucidate how these processes contribute to disease progression, tools that can directly report on protein secondary structural changes are needed. Here, we review recent studies that have demonstrated Raman spectroscopy as a powerful vibrational technique for interrogating amyloid structures; one that offers sensitivity from the global secondary structural level to specific residues. This probe-free technique is further enhanced via coupling to a microscope, which affords structural data with spatial resolution, known as Raman spectral imaging (RSI). In vitro and in cellulo applications of RSI are discussed, highlighting studies of protein droplet aging, cellular internalization of fibrils, and Raman imaging of intracellular water. Collectively, utilization of the myriad Raman spectroscopic methods will contribute to a deeper understanding of protein conformational dynamics in the complex cellular milieu and offer potential clinical diagnostic capabilities for protein misfolding and aggregation processes in disease states.

Local and global expansivity in water

The supra-molecular structure of a liquid is strongly connected to its dynamics, which in turn control macroscopic properties such as viscosity. Consequently, detailed knowledge about how this structure changes with temperature is essential to understand the thermal evolution of the dynamics ranging from the liquid to the glass. Here, we combine infrared spectroscopy (IR) measurements of the hydrogen (H) bond stretching vibration of water with molecular dynamics simulations and employ a quantitative analysis to extract the inter-molecular H-bond length in a wide temperature range of the liquid. The extracted expansivity of this H-bond differs strongly from that of the average nearest neighbor distance of oxygen atoms obtained through a common conversion of mass density. However, both properties can be connected through a simple model based on a random loose packing of spheres with a variable coordination number, which demonstrates the relevance of supra-molecular arrangement. Furthermore, the exclusion of the expansivity of the inter-molecular H-bonds reveals that the most compact molecular arrangement is formed in the range of ∼316-331K (i.e., above the density maximum) close to the temperature of several pressure-related anomalies, which indicates a characteristic point in the supra-molecular arrangement. These results confirm our earlier approach to deduce inter-molecular H-bond lengths via IR in polyalcohols [Gabriel et al. J. Chem. Phys. 154, 024503 (2021)] quantitatively and open a new alley to investigate the role of inter-molecular expansion as a precursor of molecular fluctuations on a bond-specific level.

The Near-Sightedness of Many-Body Interactions in Anharmonic Vibrational Couplings

Couplings between vibrational motions are driven by electronic interactions, and these couplings carry special significance in vibrational energy transfer, multidimensional spectroscopy experiments, and simulations of vibrational spectra. In this investigation, the many-body contributions to these couplings are analyzed computationally in the context of clathrate-like alkali metal cation hydrates, including Cs+(H2O)20, Rb+(H2O)20, and K+(H2O)20, using both analytic and quantum-chemistry potential energy surfaces. Although the harmonic spectra and one-dimensional anharmonic spectra depend strongly on these many-body interactions, the mode-pair couplings were, perhaps surprisingly, found to be dominated by one-body effects, even in cases of couplings to low-frequency modes that involved the motion of multiple water molecules. The origin of this effect was traced mainly to geometric distortion within water monomers and cancellation of many-body effects in differential couplings, and the effect was also shown to be agnostic to the identity of the ion. These outcomes provide new understanding of vibrational couplings and suggest the possibility of improved computational methods for the simulation of infrared and Raman spectra.

Path Integral Simulations of Condensed-Phase Vibrational Spectroscopy

Recent theoretical and algorithmic developments have improved the accuracy with which path integral dynamics methods can include nuclear quantum effects in simulations of condensed-phase vibrational spectra. Such methods are now understood to be approximations to the delocalized classical Matsubara dynamics of smooth Feynman paths, which dominate the dynamics of systems such as liquid water at room temperature. Focusing mainly on simulations of liquid water and hexagonal ice, we explain how the recently developed quasicentroid molecular dynamics (QCMD), fast-QCMD, and temperature-elevated path integral coarse-graining simulations (Te PIGS) methods generate classical dynamics on potentials of mean force obtained by averaging over quantum thermal fluctuations. These new methods give very close agreement with one another, and the Te PIGS method has recently yielded excellent agreement with experimentally measured vibrational spectra for liquid water, ice, and the liquid-air interface. We also discuss the limitations of such methods.

Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra

Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning, in particular, the dynamics of these systems. Atomic scale simulations can be used to predict such spectra but are often severely limited due to high computational cost or the need for strong approximations that limit the application range and reliability. Here, we introduce a machine learning (ML) accelerated approach that addresses these shortcomings and provides a significant performance boost in terms of data and computational efficiency compared with earlier ML schemes. To this end, we generalize the neuroevolution potential approach to enable the prediction of rank one and two tensors to obtain the tensorial neuroevolution potential (TNEP) scheme. We apply the resulting framework to construct models for the dipole moment, polarizability, and susceptibility of molecules, liquids, and solids and show that our approach compares favorably with several ML models from the literature with respect to accuracy and computational efficiency. Finally, we demonstrate the application of the TNEP approach to the prediction of infrared and Raman spectra of liquid water, a molecule (PTAF-), and a prototypical perovskite with strong anharmonicity (BaZrO3). The TNEP approach is implemented in the free and open source software package gpumd, which makes this methodology readily available to the scientific community.

Alkali hydroxide (LiOH, NaOH, KOH) in water: Structural and vibrational properties, including neutron scattering results

Structural and vibrational properties of aqueous solutions of alkali hydroxides (LiOH, NaOH, and KOH) are computed using quantum molecular dynamics simulations for solute concentrations ranging between 1 and 10M. Element-resolved partial radial distribution functions, neutron and x-ray structure factors, and angular distribution functions are computed for the three hydroxide solutions as a function of concentration. The vibrational spectra and frequency-dependent conductivity are computed from the Fourier transforms of velocity autocorrelation and current autocorrelation functions. Our results for the structure are validated with the available neutron data for 17M concentration of NaOH in water [Semrouni et al., Phys. Chem. Chem. Phys. 21, 6828 (2019)]. We found that the larger ionic radius [rLi+ Collapse

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Affiliation(s)

Ruru Ma Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Nitish Baradwaj Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Ken-Ichi Nomura Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Aravind Krishnamoorthy Department of Mechanical Engineering, Texas A&M University, College Station, Texas 77843-3123, USA
Rajiv K Kalia Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Aiichiro Nakano Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Priya Vashishta Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA

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Water-Hydrocarbon Interactions in Anionic Pyrene Monohydrate

Interactions between water and polycyclic aromatic hydrocarbons are essential in many aspects of chemistry, from interstellar and atmospheric processes to interfacial hydrophobicity and wetting phenomena. Despite their growing importance, the intermolecular potentials of the water-hydrocarbon interactions are underdeveloped compared to the water-water potentials, and there are similarly few experimental probes that are sensitive to the details of the water-hydrocarbon potential. We present a combined experimental and computational study of anionic pyrene monohydrate, one of the simplest water/hydrocarbon clusters. The action spectrum in the OH region of the mass-selected cluster ion provides a rigorous benchmark for intermolecular potentials and computational methodologies. We identify missing intermolecular interactions and shortcomings in conventional dynamics calculations by comparing experimental data to density functional theory and classical molecular dynamics calculations. Kinetic trapping is prevalent, even for one water molecule and one pyrene molecule, leading to slow equilibration in conventional molecular dynamics calculations, even on nanosecond time scales and at low temperatures (50 K). At constant energy, temperature fluctuations for the pair of molecules are substantial. Immersing the system in a bath of soft spheres and employing parallel tempering alleviates kinetic trapping and dampens temperature fluctuations, bringing the system closer to the thermodynamic limit. With such augmented sampling, a simple, flexible water model reproduces the line width and the asymmetric broadening of the symmetric OH stretching mode, which we assign to spectral diffusion. In the OH stretching region, dynamics calculations predict a more intense antisymmetric peak than experiments observe but do not predict the bimodal split symmetric peak that the experiments show. Our work suggests that electronic polarization, missing in the empirical force field, is responsible for the first discrepancy and that quantum nuclear effects, captured neither in density functional theory nor in classical dynamics, may be responsible for the second.

Monitoring water harvesting in metal-organic frameworks, one water molecule at a time

Metal-organic frameworks (MOFs) have gained prominence as potential materials for atmospheric water harvesting, a vital solution for arid regions and areas experiencing severe water shortages. However, the molecular factors influencing the performance of MOFs in capturing water from the air remain elusive. Among all MOFs, Ni2X2BTDD (X = F, Cl, Br) stands out as a promising water harvester due to its ability to adsorb substantial amounts of water at low relative humidity (RH). Here, we use advanced molecular dynamics simulations carried out with the state-of-the-art MB-pol data-driven many-body potential to monitor water adsorption in the three Ni2X2BTDD variants as a function of RH. Our simulations reveal that the type of halide atom in the three Ni2X2BTDD frameworks significantly influences the corresponding molecular mechanisms of water adsorption: while water molecules form strong hydrogen bonds with the fluoride atoms in Ni2F2BTDD, they tend to form hydrogen bonds with the nitrogen atoms of the triazolate linkers in Ni2Cl2BTDD and Ni2Br2BTDD. Importantly, the large size of the bromide atoms reduces the void volume in the Ni2Br2BTDD pores, which enable water molecules to initiate an extended hydrogen-bond network at lower RH. These findings not only underscore the prospect for precisely tuning structural and chemical modifications of the frameworks to optimize their interaction with water, but also highlight the predictive power of simulations with the MB-pol data-driven many-body potential. By providing a realistic description of water under different thermodynamic conditions and environments, these simulations yield unique, molecular-level insights that can guide the design and optimization of energy-efficient water harvesting materials.

Molecular driving forces for water adsorption in MOF-808: A comparative analysis with UiO-66

Metal-organic frameworks (MOFs), with their unique porous structures and versatile functionality, have emerged as promising materials for the adsorption, separation, and storage of diverse molecular species. In this study, we investigate water adsorption in MOF-808, a prototypical MOF that shares the same secondary building unit (SBU) as UiO-66, and elucidate how differences in topology and connectivity between the two MOFs influence the adsorption mechanism. To this end, molecular dynamics simulations were performed to calculate several thermodynamic and dynamical properties of water in MOF-808 as a function of relative humidity (RH), from the initial adsorption step to full pore filling. At low RH, the μ3-OH groups of the SBUs form hydrogen bonds with the initial water molecules entering the pores, which triggers the filling of these pores before the μ3-OH groups in other pores become engaged in hydrogen bonding with water molecules. Our analyses indicate that the pores of MOF-808 become filled by water sequentially as the RH increases. A similar mechanism has been reported for water adsorption in UiO-66. Despite this similarity, our study highlights distinct thermodynamic properties and framework characteristics that influence the adsorption process differently in MOF-808 and UiO-66.

Molecular Insights into the Influence of Ions on the Water Structure. I. Alkali Metal Ions in Solution

In this study, we explore the impact of alkali metal ions (Li+, Na+, K+, Rb+, and Cs+) on the hydration structure of water using molecular dynamics simulations carried out with MB-nrg potential energy functions (PEFs). Our analyses include radial distribution functions, coordination numbers, dipole moments, and infrared spectra of water molecules, calculated as a function of solvation shells. The results collectively indicate a highly local influence of all of the alkali metal ions on the hydrogen-bond network established by the surrounding water molecules, with the smallest and most densely charged Li+ ion exerting the most pronounced effect. Remarkably, the MB-nrg PEFs demonstrate excellent agreement with available experimental data for the position and size of the first solvation shells, underscoring their potential as predictive models for realistic simulations of ionic aqueous solutions across various thermodynamic conditions and environments.

Structural Stability and Disorder Level of Moderately Reduced Paper-like Graphene Oxide Investigated with Micro-Raman Analysis

This paper discusses the results of the micro-Raman analysis performed on paper-like graphene oxide (GO) samples consisting of many functionalised graphene layers and annealed at moderate temperatures (≤500 °C) under vacuum conditions (p ≃ 10-4 mbar). The analysis of the standalone samples revealed that the obtained material is characterised by a noticeable disorder level but still stays below the commonly accepted threshold of high or total disorder. GO formed in a simple way showed two spectral bands above 1650 cm-1 recorded very rarely or not at all and their origin has been discussed in detail. The results also confirmed the metastable character of multilayer GO after the annealing process at moderate temperatures as the C/O ratio was kept between 2 and 3 and the spectral features were stable within the annealing temperature range.

Predicting the Raman Spectra of Liquid Water with a Monomer-Field Model

The Raman spectrum of liquid water is quite complex, reflecting its strong sensitivity to the local environment of the individual waters. The OH-stretch region of the spectrum, which captures the influence of hydrogen bonding, has only just begun to be unraveled. Here we develop a model for predicting the Raman spectra of the OH-stretch region by considering how local electric fields distort the energy surface of each water monomer. We find that our model is capable of reproducing the bimodal nature of the main peak, with the shoulder at 3250 cm-1 resulting almost entirely from Fermi resonance. Furthermore, we capture the temperature and polarization dependence of the shoulder, which has proven to be difficult to obtain with previous methods, and analyze the origin of this dependence. We expect our model to be generally useful for understanding and predicting how Raman spectra change under different conditions and with different probe reporters beyond water.

Pitfalls in the n-mode representation of vibrational potentials

Simulations of anharmonic vibrational motion rely on computationally expedient representations of the governing potential energy surface. The n-mode representation (n-MR)-effectively a many-body expansion in the space of molecular vibrations-is a general and efficient approach that is often used for this purpose in vibrational self-consistent field (VSCF) calculations and correlated analogues thereof. In the present analysis, a lack of convergence in many VSCF calculations is shown to originate from negative and unbound potentials at truncated orders of the n-MR expansion. For cases of strong anharmonic coupling between modes, the n-MR can both dip below the true global minimum of the potential surface and lead to effective single-mode potentials in VSCF that do not correspond to bound vibrational problems, even for bound total potentials. The present analysis serves mainly as a pathology report of this issue. Furthermore, this insight into the origin of VSCF non-convergence provides a simple, albeit ad hoc, route to correct the problem by "painting in" the full representation of groups of modes that exhibit these negative potentials at little additional computational cost. Somewhat surprisingly, this approach also reasonably approximates the results of the next-higher n-MR order and identifies groups of modes with particularly strong coupling. The method is shown to identify and correct problematic triples of modes-and restore SCF convergence-in two-mode representations of challenging test systems, including the water dimer and trimer, as well as protonated tropine.

Simulating the isotropic Raman spectra of O-H stretching mode in liquid H2O based on a machine learning potential: the influence of vibrational couplings

In this study, we trained a deep potential (DP) for H2O, an accurate machine learning (ML) potential. We performed molecular dynamics (MD) simulations of liquid water using the DP model (or DeePMD simulations). Our results showed that the DP model exhibits DFT-level accuracy, and the DeePMD simulation is a promising approach for modeling the structural properties of liquid water. Based on the DeePMD simulation trajectories, we calculated the isotropic Raman spectra of the O-H stretching mode using the surface-specific velocity-velocity correlation function (ssVVCF), showing that the DeePMD/ssVVCF approach can correctly capture the bimodal characteristics of the experimental Raman spectra, with one peak located near 3400 cm-1 and the other near 3250 cm-1. The success of the DeePMD/ssVVCF approach should be credited to (1) the DFT-level accuracy of the DP model for H2O, (2) the ssVVCF formulation considering the coupling between vibrational modes, and (3) non-Condon effects. Furthermore, the DeePMD simulations revealed that the anharmonic interactions between the coupled water molecules in the first and second hydration shells should play an essential role in the strong mixing of the H-O-H bending mode and the O-H stretching mode, leading to the delocalization of the O-H stretching band. In particular, increasing the strength of hydrogen bonds would enhance the bend-stretch coupling, leading to the red-shifting of the O-H vibrational spectra and the increase in the intensity of the shoulder around 3250 cm-1.

Flow through Deformed Carbon Nanotubes Predicted by Rigid and Flexible Water Models

In this study, using nonequilibrium molecular dynamics simulation, the flow of water in deformed carbon nanotubes is studied for two water models TIP4P/2005 and simple point charge/FH (SPC/FH). The results demonstrated a nonuniform dependence of the flow on the tube deformation and the flexibility imposed on the water molecules, leading to an unexpected increase in the flow in some cases. The effects of the tube diameter and pressure gradient are investigated to explain the abnormal flow behavior with different degrees of structural deformation.

Universal machine learning for the response of atomistic systems to external fields

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a universal field-induced recursively embedded atom neural network (FIREANN) model, which integrates a pseudo field vector-dependent feature into atomic descriptors to represent system-field interactions with rigorous rotational equivariance. This "all-in-one" approach correlates various response properties like dipole moment and polarizability with the field-dependent potential energy in a single model, very suitable for spectroscopic and dynamics simulations in molecular and periodic systems in the presence of electric fields. Especially for periodic systems, we find that FIREANN can overcome the intrinsic multiple-value issue of the polarization by training atomic forces only. These results validate the universality and capability of the FIREANN method for efficient first-principles modeling of complicated systems in strong external fields.

Accelerating Anharmonic Spectroscopy Simulations via Local-Mode, Multilevel Methods

Ab initio computer simulations of anharmonic vibrational spectra provide nuanced insight into the vibrational behavior of molecules and complexes. The computational bottleneck in such simulations, particularly for ab initio potentials, is often the generation of mode-coupling potentials. Focusing specifically on two-mode couplings in this analysis, the combination of a local-mode representation and multilevel methods is demonstrated to be particularly symbiotic. In this approach, a low-level quantum chemistry method is employed to predict the pairwise couplings that should be included at the target level of theory in vibrational self-consistent field (and similar) calculations. Pairs that are excluded by this approach are "recycled" at the low level of theory. Furthermore, because this low-level pre-screening will eventually become the computational bottleneck for sufficiently large chemical systems, distance-based truncation is applied to these low-level predictions without substantive loss of accuracy. This combination is demonstrated to yield sub-wavenumber fidelity with reference vibrational transitions when including only a small fraction of target-level couplings; the overhead of predicting these couplings, particularly when employing distance-based, local-mode cutoffs, is a trivial added cost. This combined approach is assessed on a series of test cases, including ethylene, hexatriene, and the alanine dipeptide. Vibrational self-consistent field (VSCF) spectra were obtained with an RI-MP2/cc-pVTZ potential for the dipeptide, at approximately a 5-fold reduction in computational cost. Considerable optimism for increased accelerations for larger systems and higher-order couplings is also justified, based on this investigation.

MBX: A many-body energy and force calculator for data-driven many-body simulations

Many-Body eXpansion (MBX) is a C++ library that implements many-body potential energy functions (PEFs) within the "many-body energy" (MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases. MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using Open Multi-Processing and can utilize Message Passing Interface when available in interfaced molecular simulation software. MBX enables classical and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions and molecular fluids to biomolecular systems and metal-organic frameworks.

Computational infrared and Raman spectra by hybrid QM/MM techniques: a study on molecular and catalytic material systems

Vibrational spectroscopy is one of the most well-established and important techniques for characterizing chemical systems. To aid the interpretation of experimental infrared and Raman spectra, we report on recent theoretical developments in the ChemShell computational chemistry environment for modelling vibrational signatures. The hybrid quantum mechanical and molecular mechanical approach is employed, using density functional theory for the electronic structure calculations and classical forcefields for the environment. Computational vibrational intensities at chemical active sites are reported using electrostatic and fully polarizable embedding environments to achieve more realistic vibrational signatures for materials and molecular systems, including solvated molecules, proteins, zeolites and metal oxide surfaces, providing useful insight into the effect of the chemical environment on the signatures obtained from experiment. This work has been enabled by the efficient task-farming parallelism implemented in ChemShell for high-performance computing platforms.  This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.

Realistic phase diagram of water from "first principles" data-driven quantum simulations

Since the experimental characterization of the low-pressure region of water's phase diagram in the early 1900s, scientists have been on a quest to understand the thermodynamic stability of ice polymorphs on the molecular level. In this study, we demonstrate that combining the MB-pol data-driven many-body potential for water, which was rigorously derived from "first principles" and exhibits chemical accuracy, with advanced enhanced-sampling algorithms, which correctly describe the quantum nature of molecular motion and thermodynamic equilibria, enables computer simulations of water's phase diagram with an unprecedented level of realism. Besides providing fundamental insights into how enthalpic, entropic, and nuclear quantum effects shape the free-energy landscape of water, we demonstrate that recent progress in "first principles" data-driven simulations, which rigorously encode many-body molecular interactions, has opened the door to realistic computational studies of complex molecular systems, bridging the gap between experiments and simulations.

Combinational Vibration Modes in H2O/HDO/D2O Mixtures Detected Thanks to the Superior Sensitivity of Femtosecond Stimulated Raman Scattering

Overtones and combinational modes frequently play essential roles in ultrafast vibrational energy relaxation in liquid water. However, these modes are very weak and often overlap with fundamental modes, particularly in isotopologues mixtures. We measured VV and HV Raman spectra of H₂O and D₂O mixtures with femtosecond stimulated Raman scattering (FSRS) and compared the results with calculated spectra. Specifically, we observed the mode at around 1850 cm^-1 and assigned it to H-O-D bend + rocking libration. Second, we found that the H-O-D bend overtone band and the OD stretch + rocking libration combination band contribute to the band located between 2850 and 3050 cm^-1. Furthermore, we assigned the broad band located between 4000 and 4200 cm^-1 to be composed of combinational modes of high-frequency OH stretching modes with predominantly twisting and rocking librations. These results should help in a proper interpretation of Raman spectra of aqueous systems as well as in the identification of vibrational relaxation pathways in isotopically diluted water.

Matsubara dynamics approximation for generalized multi-time correlation functions

We introduce a semi-classical approximation for calculating generalized multi-time correlation functions based on Matsubara dynamics, a classical dynamics approach that conserves the quantum Boltzmann distribution. This method is exact for the zero time and harmonic limits and reduces to classical dynamics when only one Matsubara mode is considered (i.e., the centroid). Generalized multi-time correlation functions can be expressed as canonical phase-space integrals, involving classically evolved observables coupled through Poisson brackets in a smooth Matsubara space. Numerical tests on a simple potential show that the Matsubara approximation exhibits better agreement with exact results than classical dynamics, providing a bridge between the purely quantum and classical descriptions of multi-time correlation functions. Despite the phase problem that prevents practical applications of Matsubara dynamics, the reported work provides a benchmark theory for the future development of quantum-Boltzmann-preserving semi-classical approximations for studies of chemical dynamics in condensed phase systems.

Relationships between Molecular Structural Order Parameters and Equilibrium Water Dynamics in Aqueous Mixtures

Water's unique thermophysical properties and how it mediates aqueous interactions between solutes have long been interpreted in terms of its collective molecular structure. The seminal work of Errington and Debenedetti [Nature 2001, 409, 318-321] revealed a striking hierarchy of relationships among the thermodynamic, dynamic, and structural properties of water, motivating many efforts to understand (1) what measures of water structure are connected to different experimentally accessible macroscopic responses and (2) how many such structural metrics are adequate to describe the collective structural behavior of water. Diffusivity constitutes a particularly interesting experimentally accessible equilibrium property to investigate such relationships because advanced NMR techniques allow the measurement of bulk and local water dynamics in nanometer proximity to molecules and interfaces, suggesting the enticing possibility of measuring local diffusivities that report on water structure. Here, we apply statistical learning methods to discover persistent structure-dynamic correlations across a variety of simulated aqueous mixtures, from alcohol-water to polypeptoid-water systems. We investigate a variety of molecular water structure metrics and find that an unsupervised statistical learning algorithm (namely, sequential feature selection) identifies only two or three independent structural metrics that are sufficient to predict water self-diffusivity accurately. Surprisingly, the translational diffusivity of water across all mixed systems studied here is strongly correlated with a measure of tetrahedral order given by water's triplet angle distribution. We also identify a separate small number of structural metrics that well predict an important thermodynamic property, the excess chemical potential of an idealized methane-sized hydrophobe in water. Ultimately, we offer a Bayesian method of inferring water structure by using only structure-dynamics linear regression models with experimental Overhauser dynamic nuclear polarization (ODNP) measurements of water self-diffusivity. This study thus quantifies the relationships among several distinct structural order parameters in water and, through statistical learning, reveals the potential to leverage molecular structure to predict fundamental thermophysical properties. In turn, these findings suggest a framework for solving the inverse problem of inferring water's molecular structure using experimental measurements such as ODNP studies that probe local water properties.

Incorporating Polarization and Charge Transfer into a Point-Charge Model for Water Using Machine Learning

Rigid nonpolarizable water models with fixed point charges have been widely employed in molecular dynamics simulations due to their efficiency and reasonable accuracy for the potential energy surface. However, the dipole moment surface of water is not necessarily well-described by the same fixed charges, leading to failure in reproducing dipole-related properties. Here, we developed a machine-learning model trained against electronic structure data to assign point charges for water, and the resulting dipole moment surface significantly improved the predictions of the dielectric constant and the low-frequency IR spectrum of liquid water. Our analysis reveals that within our atom-centered point-charge description of the dipole moment surface, the intermolecular charge transfer is the major source of the peak intensity at 200 cm^-1, whereas the intramolecular polarization controls the enhancement of the dielectric constant. The effects of exact Hartree-Fock exchange in the hybrid density functional on these properties are also discussed.

Many-Body Effects in Aqueous Systems: Synergies Between Interaction Analysis Techniques and Force Field Development

Interaction analysis techniques, including the many-body expansion (MBE), symmetry-adapted perturbation theory, and energy decomposition analysis, allow for an intuitive understanding of complex molecular interactions. We review these methods by first providing a historical context for the study of many-body interactions and discussing how nonadditivities emerge from Hamiltonians containing strictly pairwise-additive interactions. We then elaborate on the synergy between these interaction analysis techniques and the development of advanced force fields aimed at accurately reproducing the Born-Oppenheimer potential energy surface. In particular, we focus on ab initio-based force fields that aim to explicitly reproduce many-body terms and are fitted to high-level electronic structure results. These force fields generally incorporate many-body effects through (a) parameterization of distributed multipoles, (b) explicit fitting of the MBE, (c) inclusion of many-atom features in a neural network, and (d) coarse-graining of many-body terms into an effective two-body term. We also discuss the emerging use of the MBE to improve the accuracy and speed of ab initio molecular dynamics.

Carbonic Anhydrase Enhanced UV-Crosslinked PEG-DA/PEO Extruded Hydrogel Flexible Filaments and Durable Grids for CO2 Capture

In this study, poly (ethylene glycol) diacrylate/poly (ethylene oxide) (PEG-DA/PEO) interpenetrating polymer network hydrogels (IPNH) were extruded into 1D filaments and 2D grids. The suitability of this system for enzyme immobilization and CO₂ capture application was validated. IPNH chemical composition was verified spectroscopically using FTIR. The extruded filament had an average tensile strength of 6.5 MPa and elongation at break of 80%. IPNH filament can be twisted and bent and therefore is suitable for further processing using conventional textile fabrication methods. Initial activity recovery of the entrapped carbonic anhydrase (CA) calculated from esterase activity, showed a decrease with an increase in enzyme dose, while activity retention of high enzyme dose samples was over 87% after 150 days of repeated washing and testing. IPNH 2D grids that were assembled into spiral roll structured packings exhibited increased CO₂ capture efficiency with increasing enzyme dose. Long-term CO₂ capture performance of the CA immobilized IPNH structured packing was tested in a continuous solvent recirculation experiment for 1032 h, where 52% of the initial CO₂ capture performance and 34% of the enzyme contribution were retained. These results demonstrate the feasibility of using rapid UV-crosslinking to form enzyme-immobilized hydrogels by a geometrically-controllable extrusion process that uses analogous linear polymers for both viscosity enhancement and chain entanglement purposes, and achieves high activity retention and performance stability of the immobilized CA. Potential uses for this system extend to 3D printing inks and enzyme immobilization matrices for such diverse applications as biocatalytic reactors and biosensor fabrication.

Structure and thermodynamics of water adsorption in NU-1500-Cr

Metal-organic frameworks (MOFs) are a class of materials with diverse chemical and structural properties, and have been shown to effectively adsorb various types of guest molecules. The mechanism of water adsorption in NU-1500-Cr, a high-performance atmospheric water harvesting MOF, is investigated using a combination of molecular dynamics simulations and infrared spectroscopy. Calculations of thermodynamic and dynamical properties of water as a function of relative humidity allow for following the adsorption process from the initial hydration stage to complete filling of the MOF pores. Initial hydration begins at the water molecules that saturate the open Cr³⁺ sites of the framework, which is then followed by the formation of water chains that extend along the channels connecting the hexagonal pores of the framework. Water present in these channels gradually coalesces and fills the hexagonal pores sequentially after the channels are completely hydrated. The development of hydrogen-bond networks inside the MOF pores as a function of relative humidity is characterized at the molecular level using experimental and computational infrared spectroscopy. A detailed analysis of the OH-stretch vibrational band indicates that the low-frequency tail stems from strongly polarized hydrogen-bonded water molecules, suggesting the presence of some structural disorder in the experimental samples. Strategies for designing efficient water harvesting MOFs are also proposed based on the mechanism of water adsorption in NU-1500-Cr.

Two-dimensional infrared-Raman spectroscopy as a probe of water's tetrahedrality

Two-dimensional spectroscopic techniques combining terahertz (THz), infrared (IR), and visible pulses offer a wealth of information about coupling among vibrational modes in molecular liquids, thus providing a promising probe of their local structure. However, the capabilities of these spectroscopies are still largely unexplored due to experimental limitations and inherently weak nonlinear signals. Here, through a combination of equilibrium-nonequilibrium molecular dynamics (MD) and a tailored spectrum decomposition scheme, we identify a relationship between the tetrahedral order of liquid water and its two-dimensional IR-IR-Raman (IIR) spectrum. The structure-spectrum relationship can explain the temperature dependence of the spectral features corresponding to the anharmonic coupling between low-frequency intermolecular and high-frequency intramolecular vibrational modes of water. In light of these results, we propose new experiments and discuss the implications for the study of tetrahedrality of liquid water.

Is Unified Understanding of Vibrational Coupling of Water Possible? Hyper-Raman Measurement and Machine Learning Spectra

The impact of the vibrational coupling of the OH stretch mode on the spectra differs significantly between IR and Raman spectra of water. Unified understanding of the vibrational couplings is not yet achieved. By using a different class of vibrational spectroscopy, hyper-Raman (HR) spectroscopy, together with machine-learning-assisted HR spectra calculation, we examine the impact of the vibrational couplings of water through the comparison of isotopically diluted H₂O and pure H₂O. We found that the isotopic dilution reduces the HR bandwidths, but the impact of the vibrational coupling is smaller than in the IR and parallel-polarized Raman. Machine learning HR spectra indicate that the intermolecular coupling plays a major role in broadening the bandwidth, while the intramolecular coupling is negligibly small, which is consistent with the IR and Raman spectra. Our result clearly demonstrates a limited impact of the intramolecular vibration, independent of the selection rules of vibrational spectroscopies.

Comparison of Matsubara dynamics with exact quantum dynamics for an oscillator coupled to a dissipative bath

We report the first numerical calculations in which converged Matsubara dynamics is compared directly with exact quantum dynamics with no artificial damping of the time-correlation functions (TCFs). The system treated is a Morse oscillator coupled to a harmonic bath. We show that, when the system-bath coupling is sufficiently strong, the Matsubara calculations can be converged by explicitly including up to M = 200 Matsubara modes, with the remaining modes included as a harmonic "tail" correction. The resulting Matsubara TCFs are in near-perfect agreement with the exact quantum TCFs, for non-linear as well as linear operators, at a temperature at which the TCFs are dominated by quantum thermal fluctuations. These results provide compelling evidence that incoherent classical dynamics can arise in the condensed phase at temperatures at which the statistics are dominated by quantum (Boltzmann) effects, as a result of smoothing of imaginary-time Feynman paths. The techniques developed here may also lead to efficient methods for benchmarking system-bath dynamics in the overdamped regime.

Dispersion related coupling effects in IR spectra on the example of water and Amide I bands

We discuss coupling effects in infrared spectra which are caused by dispersion and local field effects. The first effect is instigated by changes of the refractive index due to absorption which have an impact on the strength of adjacent absorptions. The second effect is a consequence of the light-induced polarization of one molecule affecting neighboring ones. These coupling effects do not only effect band positions, but also influence relative intensities. They are particularly strong in case of overlapping bands and complicate their deconvolution by band fitting. We investigated the corresponding challenges for the HO-stretching vibrations in water and the Amide I band in proteins. Our findings show that the effects are significant and of high interest for protein and water structure determination. Especially, for the water stretching vibrations we conclude that it is of utmost importance to consider such coupling effects in quantum mechanical calculations of water spectra. Otherwise, progress in understanding band positions and profiles is likely to be hampered. Also, in case of the Amide I band we found a distinct impact of such coupling effects. Accordingly, we strongly recommend consideration of dispersion and local field effects to ensure the possibility of an accurate, quantitative determination of α-helix and β-sheet structures.

Accurate and Interpretable Dipole Interaction Model-Based Machine Learning for Molecular Polarizability

Polarizabilities play significant roles in describing dispersive and inductive interactions of the atom and molecular systems. However, an accurate prediction of molecular polarizabilities from first principles is computationally prohibitive. Although physical models or statistical machine learning models have been proposed, either a lack of accurate description of local chemical environments or demanding a large number of samples for training has limited their practical applications. In this study, we combine a physically inspired dipole interaction model and an accurate neural network method for predicting the polarizability tensors of molecules. With the local chemical environment precisely described and the requirement of rotational covariance naturally fulfilled, this hybrid model is proven to give an accurate molecular polarizability prediction, essentially reducing the number of training samples. The atomic polarizabilities are physically interpretable and transferable to larger molecules unseen in the training set. This promising method may find its wide range of applications, such as spectroscopic simulations and the construction of polarizable force fields.