785 papers
This collection explores the fascinating intersection where the laws of physics meet the complex machinery of chemistry. Here, researchers investigate how quantum mechanics governs molecular bonds, how light interacts with matter at the atomic scale, and how fundamental forces shape chemical reactions. It is a realm where abstract mathematical models collide with tangible substances to reveal the hidden mechanisms driving our material world.
On Gist.Science, we process every new preprint in this category directly from arXiv to make these discoveries accessible to everyone. Whether you are a seasoned expert or a curious reader, you will find both plain-language explanations and detailed technical summaries for each paper. Below are the latest contributions from the community pushing the boundaries of physical chemistry.
This paper demonstrates that optimizing site-dependent dephasing profiles can enhance steady-state quantum transport in one-dimensional lattices by overcoming localization and energy detuning more effectively than spatially uniform noise.
This paper numerically investigates how fluid flow conditions and porous media properties influence drug release from drug-filled porous implants (DFPIs), demonstrating that these factors can be leveraged to design intelligent implants with customized, time-dependent release profiles.
This paper theoretically demonstrates that optical phonons in graphene significantly suppress high harmonic generation (HHG) yields and drive electronic decoherence through phase scrambling and interband current coupling, providing a key explanation for the observed limitations in experimental HHG spectra.
This paper introduces two new variants of the structure factor twist averaging (sfTA) method, "paired sfTA" and "binding sfTA," which improve the accuracy of calculating binding correlation energies in low-dimensional bilayer materials by incorporating binding interactions into the twist-angle selection process.
This paper demonstrates how broadband impulsive stimulated Raman spectroscopy, enhanced by new chirp correction and wavelet analysis methods, can disentangle complex spectral data to reveal state-specific vibronic couplings and the transfer of vibrational coherence between electronic states mediated by nonadiabatic coupling.
By applying machine-learned symbolic regression to molecular dynamics data, this study identifies a nonlinear, multi-dihedral reaction coordinate for retinal isomerization, revealing that the reaction follows a stepwise, S-shaped dynamical pathway that is not captured by the minimum-free-energy surface.
Vib2Conf is a deep learning model that utilizes an attentional resampler and Mixture-of-Experts to achieve high-precision discrimination of 3D molecular conformations from vibrational spectra, even distinguishing between near-isomeric conformers.
This paper demonstrates a computational workflow combining machine learning, quantum chemistry, and atomistic simulations to identify and experimentally validate light-activated PARP1 inhibitors, successfully producing a candidate that shows a 15-fold increase in inhibition upon green-light irradiation.
This paper demonstrates that boron doping in layered cathodes enhances specific capacity, cycling stability, and sodium-ion diffusion kinetics through a combination of electrochemical testing, DRT analysis, and computational simulations (DFT and MD).
This paper proposes a scalable divide-and-conquer heuristic that decomposes large-scale molecular docking problems into smaller sub-problems, allowing neutral-atom quantum processors to solve complex protein-ligand binding tasks that would otherwise exceed their capacity.