1044 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 presents a highly precise and efficient experimental method for determining the ionization energies and work functions of temperature-controlled alkali metal nanoparticles in a beam by combining a stable condensation source, thermalization, tunable photoionization, and automated data analysis using the universal Fowler function.
This study demonstrates that high-resolution photoionization threshold measurements of 7–9 nm sodium and potassium nanoparticles can detect thermal expansion and a distinct melting transition, revealing a melting point suppression of nearly 100 K that aligns with Gibbs-Thomson predictions.
By measuring the work functions of 6Li and 7Li nanoparticles via photoionization, researchers discovered a significant isotope effect and anomalous temperature dependence that highlight lithium's complex quantum nature and confirm the Third Law of thermodynamics prediction that the work function slope vanishes at low temperatures.
This paper presents a novel reactive coarse-grained force field, nb-CG-ZIF-FF, derived via multiscale methods that successfully models the self-assembly and structural evolution of ZIF-8 by learning tetrahedral connectivity from atomistic benchmarks without explicit connectivity constraints, thereby offering a scalable approach to study MOF formation and dynamics.
This paper demonstrates that for an ensemble of independent finite-speed searchers, the mean fastest first-passage time to a target is bounded below by the minimal ballistic travel time and converges exponentially to this limit as the number of searchers increases, revealing a significant efficiency advantage over Brownian searchers and correcting misconceptions about short-time behavior in diffusive models.
This study demonstrates that positively ionized medium-sized gold clusters (22–100 atoms) preferentially adopt planar ground states over compact structures due to charge-induced effects and finite-temperature stabilization, a finding achieved by adapting the Minima Hopping algorithm with a machine-learned potential corrected for Coulomb interactions.
This paper demonstrates that while explicit optimization of a negative log-likelihood loss is crucial for calibrating force uncertainties in shallow ensembles for machine learning interatomic potentials, a computationally efficient fine-tuning protocol can achieve comparable calibration quality with up to 96% reduced training time across diverse materials.
This paper introduces a C++-based next generation of the SIMPSON software package featuring novel capabilities for simulating advanced NMR, EPR, and pulsed DNP experiments, including propagator splitting, pulse transients, and quadrupolar cross terms, while improving computational speed and facilitating community contributions.
This paper argues that the widely used endpoint slippage analysis method for quantifying parasitic side-reactions in batteries can yield inaccurate results for specific electrode systems like silicon and hard carbon, and it proposes corrected equations to derive true reaction rates from experimental data.
This paper derives a first-principles variational functional theory incorporating one-loop and local-density approximations to capture Coulombic correlations in electrolytes, demonstrating that embedding this model into a quantum-classical framework significantly improves the prediction of interfacial capacitance at metal-electrolyte interfaces by revealing a pronounced double-peak structure consistent with experimental data.