The Journal of Physical Chemistry Letters最新文献

The intricate mechanisms underlying electron transfer and structural evolution are essential to understanding the oxidation dynamics of transition metal atoms; however, accurately measuring the mechanisms remains challenging. In this study, utilizing laser ablation-crossed beam and time-sliced ion velocity imaging techniques, we identified two distinct electron transfer mechanisms in the reactions of Y and O₂ based on reactive and nonreactive scattering measurements across varying collision energies. (1) Low-barrier end-on pathway: Electron transfer occurs through a collinear Y–O–O geometry with a low activation barrier, evidenced by rebound scattering of YO products at low collision energy and backward scattering of Y reactants at higher energies. (2) High-barrier side-on pathway: Electron transfer proceeds through a side-on geometry, presenting a higher activation barrier that facilitates the formation of long-lived O–Y–O intermediates, which is characterized by the backward-forward peaking angular distribution of YO products and broad energy distributions of O₂ reactants at high collision energy.

The elementary steps in the rotation of several second-generation molecular motors are analyzed by finding the minimum energy path between the metastable and stable states and evaluating the transition rate within harmonic transition state theory based on energetics obtained from density functional theory. Comparison with published experimental data shows remarkably good agreement and demonstrates the predictive capability of this approach. While previous measurements by Feringa and co-workers have shown that a replacement of the hydrogen atom at the stereogenic center by a fluorine atom can slow down the rate-limiting thermal helix inversion (THI) step by raising the energy of the transition state, even to the extent that the backreaction in the ground state becomes preferred in some cases, we find that a replacement of a CH₃ group by CF₃ at the same site accelerates the THI by elevating the energy of the metastable state without affecting the transition state significantly. Since these two fluorine substitutions have an opposite effect on the rate of the THI, the combination of both can provide ways to fine-tune the rotational speed of molecular motors.

We studied through molecular dynamics and inhomogeneous solubility-diffusion theory the permeability of several cyclic peptides (CPs) recently proposed as potential antisickling drugs, across a red blood cell (RBC) membrane model. The permeability of the CPs is compared to that of a linear precursor, a highly charged CP, a high permeability halogenated antisickling molecule (PD150606), and water. The influence of cholesterol (45% of the membrane) is assessed through comparison with the permeability across a homogeneous lipid bilayer. The most promising CPs concerning their potential antisickling activity depict the highest permeabilities, only exceeded by PD150606. The permeability of a hydrophobic CP is four decades higher than its linear precursor despite noncovalent cyclization in the interior of the membrane. Further, cholesterol is found to significantly reduce the permeability of water and a model CP, while not influencing that of PD150606. The influence of the water model is also investigated.

Developing machine learning protocols for molecular simulations requires comprehensive and efficient data sets. Here we introduce the QMe14S data set, comprising 186,102 small organic molecules featuring 14 elements (H, B, C, N, O, F, Al, Si, P, S, Cl, As, Se, and Br) and 47 functional groups. Using density functional theory at the B3LYP/TZVP level, we optimized the geometries and calculated properties, including energy, atomic charge, atomic force, dipole moment, quadrupole moment, polarizability, octupole moment, first hyperpolarizability, and Hessian. At the same level, we obtained the harmonic IR, Raman, and NMR spectra. Furthermore, we conducted ab initio molecular dynamics simulations to generate dynamic configurations and extract nonequilibrium properties, including energy, forces, and Hessians. By leveraging our E(3)-equivariant message-passing neural network (DetaNet), we demonstrated that models trained on QMe14S outperform those trained on the previously developed QM9S data set in simulating molecular spectra. The QMe14S data set thus serves as a comprehensive benchmark for molecular simulations, offering valuable insights into structure–property relationships.