Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity

Quantitative Prediction of Exchangeable Proton Chemical Shifts

Accurately predicting NMR chemical shifts of exchangeable protons in solution remains challenging because of the combined influence of solute–solvent interactions and molecular dynamics. We introduce a framework that integrates machine-learning molecular dynamics (ML-MD) with the ShiftML3 machine-learning shielding model for rapid and accurate prediction of NMR spectra in solvated molecules. Although originally developed for solids, ShiftML3 effectively captures intermolecular contributions to shielding in solution. We validate the method across a range of chemically diverse systems, including water in organic solvents, solvated alcohols, hydrogen-bonded nucleobases, glucose anomers, and alkylated acetamides. The ML-MD + ShiftML3 framework reproduces experimentally observed chemical shifts of exchangeable protons with near-quantitative accuracy, resolving subtle hydrogen-bonding and conformational effects that implicit-solvent DFT fails to capture. These results establish ML-MD + ShiftML3 as a transferable and computationally efficient way of incorporating solvation and dynamics into NMR spectroscopy, enabling realistic chemical shift predictions for flexible, hydrogen-bonded, and complex molecular systems. The authors develop a framework integrating machine-learning molecular dynamics with the ShiftML3 machine-learning shielding mode to accurately predict NMR chemical shifts of exchangeable protons in solution, outperforming DFT approaches in capturing solvation effects.

High-Fidelity Surrogate Framework for Rapid Solvation of Gas-Phase and Interfacial Reaction Potential Energy Surfaces.

Solvent can reorganize reaction paths and barriers, but bringing those effects into mechanistic calculations remains expensive, because every image in a solvated climbing-image nudged elastic band search requires a new self-consistent reaction-field calculation. Here, we present a high-fidelity surrogate framework that keeps a preconstructed nonsolvated reaction valley as the backbone and models only the geometry-dependent solvent contribution needed to transform it into the solution-phase potential of mean force, i.e., the solvent-shaped free-energy surface. Using uncertainty-selected evaluations with the plane-wave implicit solvation model based on electron density (PW-SMD), the method optimizes solvated reaction paths on the fly and recovers 300 K free-energy profiles from local surrogate Hessians. For 16 small-molecule reactions in water and 17 Cu(111) interfacial reactions, the framework reduces the average number of solvent-response calculations from 826.3 to 67.3 and from 1231.9 to 51.5, respectively, while maintaining small-molecule barrier differences of only 0.04 eV on average and an interfacial reaction barrier agreement within a few hundredths of an eV for most reactions. The surrogate also reproduces representative room-temperature activation and reaction free energies. These results show that solvent-induced reshaping of reaction pathways can be captured accurately from a sparse, strategically chosen set of solution-phase calculations, turning solvated reaction-path and free-energy calculations into a practical tool for molecular and interfacial mechanistic studies.

Solvent effects on the structures and binding energies of arsine clusters (AsH3)n (n = 2–6): a DFT study with implicit solvation

Development of United-Atom Force Fields for Monomeric and Oligomeric Ionic Liquids through Regression-Guided Optimization of Electronic Continuum Correction Parameters

Ionic liquids are salts that exist in the liquid state at room temperature and exhibit high viscosity because of their strong electrostatic interactions. It was difficult to reproduce their viscosity by molecular dynamics simulations with conventional nonpolarizable force fields; however, recent development of force fields implementing electronic continuum correction (ECC), which accounts for polarizability, has enabled accurate predictions. Here, we present a regression-guided strategy to optimize scaling factors for ECC charges and Lennard-Jones parameters for monomeric (BMIM+, MOEMIM+) and oligomeric (IL22+, IL44+) imidazolium-based cations paired with TFSI– with united-atom models. The scaling factors were optimized to simultaneously reproduce experimental density and viscosity. To validate the strategy, we calculated the temperature dependence of density, diffusion coefficient, conductivity, and viscosity of BMIM–TFSI, achieving good agreements with experiments. Moreover, scaling factors optimized for MOEMIM+, which shares similar chemical structure and elemental compositions with IL22+ and IL44+, were found to be transferable to these compounds. Thus, this work not only provides a practical regression-guided workflow for selecting ECC-based united-atom force-field parameters but also suggests their transferability across chemically related ionic liquids.

Free Energies of Solvation in Cyclohexane and Water: United-Atom vs All-Atom.

Free energies of solvation (ΔGsol) in a prototypical liquid alkane, cyclohexane, have been computed for 101 organic molecules at 25 °C. Monte Carlo statistical mechanics (MC) was used with free-energy perturbation theory (FEP) and both OPLS united-atom (UA) and all-atom (AA) force fields. Updated OPLS-UA parameters are provided along with thermodynamic results for 23 liquid alkanes; the modifications make the UA and AA force fields fully compatible. The average errors for ΔGsol in comparison to experimental data are ca. 0.5 kcal/mol for both force fields. This supports general use of the UA model, since it reduces the required computation times by 5-10-fold. The largest errors are about 1 kcal/mol and occur for small molecules with relatively large dipole moments and for perfluorocarbons. The former case is attributable to the lack of solvent-polarization in the force fields, and the latter issue can be remedied by reducing the Lennard-Jones well depth for the interaction of saturated carbon and fluorine. Results for free energies of hydration are also provided for the 101 solutes in TIP4P water and the average error is again 0.5 kcal/mol. The combined results provide cyclohexane/water free energies of transfer with average errors of 0.7 kcal/mol. In conjunction with prior results for solvation in benzene and perfluorobenzene, the 0.5 kcal/mol level of accuracy seems general for the performance of current generation, nonpolarizable force fields. Implications for modeling hydrophobic effects and protein-ligand binding are also considered.

A Holistic View of Water Environments in Molecular Crystals

Hydrates are common solid forms that can significantly affect a compound’s stability, physicochemical properties, and commercial viability. Despite their importance in pharmaceutical development, hydrate structures, their relationship to material properties, and their propensity for formation remain poorly understood. In 2003, Gillon et al. introduced a simple yet powerful framework for the structural classification of hydrates based on the hydrogen-bonding environment of crystalline water molecules. This framework categorizes water molecules according to the number of hydrogen-bond donor (D) and acceptor (A) interactions in which they participate, giving rise to eight distinct environments. In the original study, the DDA environment, where water donates two hydrogen bonds and accepts one, was reported as the most common. The statistical distribution of these environments in the Cambridge Structural Database (CSD) has since provided a useful benchmark for the qualitative assessment of hydrate structures. Here, we revisit Gillon’s water environment classification using a substantially expanded data set comprising 13,881 hydrate entries from the CSD and provide updated statistics on hydrate structures. Our analysis confirms that the DDA environment remains the most prevalent, followed by DDAA and DD, while the remaining environments occur less frequently. Extending beyond the original work, we quantify the energetics associated with each environment and demonstrate that the four-hydrogen-bond DDAA environment is energetically the most favorable, followed by environments involving three, two, and one hydrogen bonds, respectively. We further show that water-mediated intermolecular interactions contribute up to 40% of the total lattice interaction energy in the hydrate structures, highlighting the surprisingly large influence of this small solvent molecule on crystal stability. Despite its superior energetic stability, the DDAA environment is not the most frequently observed experimentally. This apparent discrepancy arises because the hydrogen-bonding capability and topology of the main component in the hydrate constrains the maximum hydrogen-bonding environment that water can achieve. Overall, this work provides a comprehensive analysis of hydrate structures and energetics across the CSD. The resulting insights offer a valuable framework for the qualitative assessment of newly discovered hydrate forms and for evaluating whether their structures conform to established crystallographic trends.