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 study resolves discrepancies in the kinetics of the NH2 + O(3P) reaction by constructing a full-dimensional, machine-learned potential energy surface using high-level ic-MRCI calculations, which enables accurate quasi-classical trajectory simulations of thermal rate coefficients and branching ratios essential for refining nitrogen-fuel combustion models.
This study establishes a highly accurate, sub-cm⁻¹ benchmark potential energy surface for the helium-benzene complex by integrating high-level CCSD(T) and SAPT calculations with multifidelity Gaussian process regression, revealing that this new potential predicts qualitatively different low-temperature solvation behaviors compared to traditional empirical models.
This paper presents a theoretical statistical model incorporating Herzberg-Teller and Duschinsky rotation effects to calculate recurrent fluorescence rates in PAH cations, revealing that symmetry-forbidden transitions may significantly enhance their cooling efficiency and stability in the interstellar medium.
This paper introduces and benchmarks a new one-parameter atomic-number-based damping function (XDM(Z)) for dispersion corrections, demonstrating that when paired with specific hybrid functionals like revPBE0 and B86bPBE0, it achieves consistent high accuracy across the comprehensive GMTKN55 molecular database and molecular crystal benchmarks.
The paper introduces Nexerra-R1, a building-block chemical language model that enables the inverse design of reticular materials by generating targeted organic linkers and assembling them into synthesizable metal-organic frameworks, successfully demonstrating this approach by rediscovering known structures and proposing a novel framework, CU-525.
This paper introduces a reusable geometry-optimization engine in PyBEST that interfaces with \texttt{geomeTRIC} to provide the first implementation of analytic nuclear gradients for orbital-optimized pair coupled-cluster doubles (OOpCCD), enabling robust and accurate molecular structure optimization for seniority-zero wavefunctions.
This study demonstrates that using highly converged electronic structure calculations is essential for accurately modeling liquid water and ice, revealing that commonly used pragmatic settings often yield misleading agreement with experiments while more rigorous approaches significantly improve predictive accuracy.
This paper presents a unified machine learning framework that combines machine-learned interatomic potentials with neural classical density functional theory to enable efficient, first-principles multiscale modeling of liquid thermodynamics and phase behavior across homogeneous and inhomogeneous systems.
This paper resolves significant discrepancies in molecular simulation predictions of disjoining pressure for liquid nanofilms by demonstrating that neglecting long-range dispersion interactions and using inconsistent film thickness definitions in the original Peng method leads to errors, which are corrected by a revised approach that accounts for thickness-dependent surface tension effects and aligns with the Bhatt method.
This paper proposes a geometric diagnostic for quantum scrambling sensitivity by utilizing Lagrangian Descriptors within a Bohmian trajectory framework over a two-dimensional preparation space of Gaussian wavepackets, demonstrating that for the inverted harmonic oscillator, this approach yields an exponential sensitivity bound comparable to Out-of-Time-Order Correlator (OTOC) growth while circumventing the uncertainty principle's obstruction to defining independent initial position and momentum.