1032 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 introduces a systematic protocol combining constraint enforcement, flexible functional forms, and modern optimization to develop the COACH functional, a range-separated hybrid meta-GGA that achieves superior accuracy and transferability across molecular benchmarks while highlighting the need for nonlocal information to overcome current performance limits.
This paper presents a method for accurately calculating local temperatures in molecular dynamics simulations with rigid constraints by self-consistently evaluating the degrees of freedom, thereby correcting unphysical violations of kinetic energy equipartition and providing a sensitive indicator for numerical integration errors or insufficient equilibration.
This paper demonstrates that a computationally efficient workflow combining DFT interface calculations with Koopmans spectral functionals can accurately predict the band structures and level alignments of rutile, anatase, and brookite TiO, offering a promising strategy for screening novel photocatalysts for water splitting.
This study combines experimental two-color four-wave mixing measurements with theoretical Quantum Master Equation simulations to demonstrate that in liquid nitrobenzene, near-infrared pulses simultaneously launch librational motion and create electronic coherences, resulting in a non-parametric nonlinear optical signal that leaves molecules in an excited electronic state.
This paper surveys 35 independent DMRG software packages, highlighting significant feature overlaps and advocating for greater modularity and standardization to reduce duplication, improve interoperability, and enable the tackling of more complex quantum many-body problems.
This study combines density functional theory calculations with experimental data to elucidate the structural and hyperfine magnetic properties of Ni- and Cr-doped cementite, specifically determining impurity site preferences, analyzing the formation mechanism of hyperfine fields, and validating Mössbauer spectroscopy approximations.
This paper proposes a transferable deep learning method that uses E(3)-equivariant neural networks to predict electron densities in a compact auxiliary basis, achieving robust acceleration of Density Functional Theory calculations for systems up to 900 atoms without retraining, thereby overcoming the generalization failures of existing Hamiltonian-based approaches.
This paper establishes that high-resolution NMR spectra exhibit mirror symmetry about the mid-resonance frequency if and only if the spin resonant frequencies are symmetrically positioned and the J coupling matrix is symmetric about the secondary diagonal, a finding validated through theoretical calculations for 4-, 5-, and 6-spin systems.
This paper introduces COFAP, a universal deep learning framework that leverages multi-modal feature extraction and cross-modal attention to achieve state-of-the-art, gas-agnostic prediction of covalent organic framework (COF) adsorption performance, thereby enabling efficient high-throughput screening and application-specific prioritization of candidate materials.
This paper proposes a simple and accurate method to predict the random close packing fraction of binary hard-disk mixtures by introducing a parameter based on the reduced third virial coefficient, which captures three-body correlations and yields a near-linear relationship that outperforms existing models across various size ratios and compositions.