Understanding the Dipole Moment of Liquid Water from a Self-Attractive Hartree Decomposition

Solubility and thermodynamics of mesalazine in aqueous mixtures of poly ethylene glycol 200/600 at 293.2-313.2K

In this study, the solubility of mesalazine was investigated in binary solvent mixtures of poly ethylene glycols 200/600 and water at temperatures ranging from 293.2K to 313.2K. The solubility of mesalazine was determined using a shake-flask method, and its concentrations were measured using a UV-Vis spectrophotometer. The obtained solubility data were analyzed using mathematical models including the van't Hoff, Jouyban-Acree, Jouyban-Acree-van't Hoff, mixture response surface, and modified Wilson models. The experimental data obtained for mesalazine dissolution encompassed various thermodynamic properties, including ΔG°, ΔH°, ΔS°, and TΔS°. These properties offer valuable insights into the energetic aspects of the dissolution process and were calculated based on the van't Hoff equation.

Repulsive Lateral Interaction of Water Molecules at the Initial Stages of Adsorption in Microporous AlPO4-11 According to 27Al NMR and DFT

Lateral (adsorbate-adsorbate) interactions between adsorbed molecules affect various physical and chemical properties of microporous adsorbents and catalysts, influencing their functional properties. In this work, we studied the hydration of microporous AlPO4-11 aluminophosphate, which has an unusually ordered structure upon adsorption of water vapor, and according to 27Al NMR data, only tetrahedrally or octahedrally coordinated Al sites are present in the AlPO4-11. These 27Al NMR data are consistent with the results of density functional theory (DFT) calculations of hydrated AlPO4-11, which revealed the presence of a strong repulsive lateral interaction at the initial stage of adsorption, suppressing the adsorption of water on neighboring (separated by one -O-P-O- bridge) Al crystallographic sites. As a result, of all the different aluminum sites, only half of the Al1 sites adsorb two water molecules and acquire octahedral coordination.

Orientational Behavior and Vibrational Response of Glycine at Aqueous Interfaces

Aqueous glycine plays many different roles in living systems, from being a building block for proteins to being a neurotransmitter. To better understand its fundamental behavior, we study glycine's orientational behavior near model aqueous interfaces, in the absence and presence of electric fields and biorelevant ions. To this purpose, we use a surface-specific technique called heterodyne-detected vibrational sum-frequency generation spectroscopy (HD-VSFG). Using HD-VSFG, we directly probe the symmetric and antisymmetric stretching vibrations of the carboxylate group of zwitterionic glycine. From their relative amplitudes, we infer the zwitterion's orientation near surfactant-covered interfaces and find that it is governed by both electrostatic and surfactant-specific interactions. By introducing additional ions, we observe that the net orientation is altered by the enhanced ionic strength, indicating a change in the balance of the electrostatic and surfactant-specific interactions.

Machine Learning Many-Body Green's Functions for Molecular Excitation Spectra

We present a machine learning (ML) framework for predicting Green's functions of molecular systems, from which photoemission spectra and quasiparticle energies at quantum many-body level can be obtained. Kernel ridge regression is adopted to predict self-energy matrix elements on compact imaginary frequency grids from static and dynamical mean-field electronic features, which gives direct access to real-frequency many-body Green's functions through analytic continuation and Dyson's equation. Feature and self-energy matrices are represented in a symmetry-adapted intrinsic atomic orbital plus projected atomic orbital basis to enforce rotational invariance. We demonstrate good transferability and high data efficiency of the proposed ML method across molecular sizes and chemical species by showing accurate predictions of density of states (DOS) and quasiparticle energies at the level of many-body perturbation theory (GW) or full configuration interaction. For the ML model trained on 48 out of 1995 molecules randomly sampled from the QM7 and QM9 data sets, we report the mean absolute errors of ML-predicted highest occupied and lowest unoccupied molecular orbital energies to be 0.13 and 0.10 eV, respectively, compared to GW@PBE0. We further showcase the capability of this method by applying the same ML model to predict DOS for significantly larger organic molecules with up to 44 heavy atoms.

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

This manuscript provides an overview of the current state of the art in terms of the molecular modelling of the thermophysical properties of fluids. It is intended to manage the expectations and serve as guidance to practising physical chemists, chemical physicists and engineers in terms of the scope and accuracy of the more commonly available intermolecular potentials along with the peculiarities of the software and methods employed in molecular simulations while providing insights on the gaps and opportunities available in this field. The discussion is focused around case studies which showcase both the precision and the limitations of frequently used workflows.

Transport of Organic Volatiles through Paper: Physics-Informed Neural Networks for Solving Inverse and Forward Problems

Transport of volatile organic compounds (VOCs) through porous media with active surfaces takes place in many important applications, such as in cellulose-based materials for packaging. Generally, it is a complex process that combines diffusion with sorption at any time. To date, the data needed to use and validate the mathematical models proposed in literature to describe the mentioned processes are scarce and have not been systematically compiled. As an extension of the model of Ramarao et al. (Dry Technol 21(10):2007–2056, 2003) for the water vapor transport through paper, we propose to describe the transport of VOCs by a nonlinear Fisher–Kolmogorov–Petrovsky–Piskunov equation coupled to a partial differential equation (PDE) for the sorption process. The proposed PDE system contains specific material parameters such as diffusion coefficients and adsorption rates as multiplication factors. Although these parameters are essential for solving the PDEs at a given time scale, not all of the required parameters can be directly deduced from experiments, particularly diffusion coefficients and sorption constants. Therefore, we propose to use experimental concentration data, obtained for the migration of dimethyl sulfoxide (DMSO) through a stack of paper sheets, to infer the sorption constant. These concentrations are considered as the outcome of a model prediction and are inserted into an inverse boundary problem. We employ Physics-Informed Neural Networks (PINNs) to find the underlying sorption constant of DMSO on paper from this inverse problem. We illustrate how to practically combine PINN-based calculations with experimental data to obtain trustworthy transport-related material parameters. Finally we verify the obtained parameter by solving the forward migration problem via PINNs and finite element methods on the relevant time scale and show the satisfactory correspondence between the simulation and experimental results.

Linear Combination of Atomic Dipoles to Calculate the Bond and Molecular Dipole Moments of Molecules and Molecular Liquids

We report a linear combination of atomic dipole (LCAD) method for calculating the bond dipole moments of molecules. We show that the LCAD method reproduces the known molecular dipole moments of small to large molecules with a small error with respect to experimental and benchmark ab initio calculations, and molecular dipole distributions of bulk water that agree with maximally localized Wannier functions. The bond dipole moments derived from LCAD are also chemically interpretable in terms of the trend in bond ionicity in going from neutral to charged molecules. Moreover, the LCAD method accurately captures the influence of electric fields, supported by the correct trend in the change of the dipole moment under a uniform external electric field. The better grounding of bond dipole calculations indicates that it should also serve as a useful approach to bond dipole-field models used in catalysis or to reconstruct the small dipole of a H-terminated graphene flake.

Machine learning inference of molecular dipole moment in liquid water

Molecular dipole moment in liquid water is an intriguing property, partly due to the fact that there is no unique way to partition the total electron density into individual molecular contributions. The prevailing method to circumvent this problem is to use maximally localized Wannier functions, which perform a unitary transformation of the occupied molecular orbitals by minimizing the spread function of Boys. Here we revisit this problem using a data-driven approach satisfying two physical constraints, namely: (a) The displacement of the atomic charges is proportional to the Berry phase polarization; (b) Each water molecule has a formal charge of zero. It turns out that the distribution of molecular dipole moments in liquid water inferred from latent variables is surprisingly similar to that obtained from maximally localized Wannier functions. Apart from putting a maximum-likelihood footnote to the established method, this work highlights the capability of graph convolution based charge models and the importance of physical constraints on improving the model interpretability.

Decomposed Mean-Field Simulations of Local Properties in Condensed Phases

The present work demonstrates a robust protocol for probing localized electronic structure in condensed-phase systems, operating in terms of a recently proposed theory for decomposing the results of Kohn-Sham density functional theory in a basis of spatially localized molecular orbitals. In an initial application to liquid, ambient water and the assessment of the solvation energy and the embedded dipole moment of H₂O in solution, we find that both properties are amplified on average-in accordance with expectation-and that correlations are indeed observed to exist between them. However, the simulated solvent-induced shift to the dipole moment of water is found to be significantly dampened with respect to typical literature values. The local nature of our methodology has further allowed us to evaluate the convergence of bulk properties with respect to the extent of the underlying one-electron basis set, ranging from single-ζ to full (augmented) quadruple-ζ quality. Albeit a pilot example, our work paves the way toward future studies of local effects and defects in more complex phases, e.g., liquid mixtures and even solid-state crystals.