946 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 develops and validates a specialized machine learning interatomic potential (QCOF) based on the MACE architecture to efficiently simulate the thermal and mechanical properties of defective covalent organic frameworks, revealing distinct defect sensitivities in CTF-1 and COF-LZU1 systems while establishing a robust framework for large-scale quantum-accurate modeling of extended network materials.
This paper demonstrates that existing methods fail to quantify rotational motion in complex molecular systems like supercooled liquids and introduces a new empirical method that accurately captures the full spectrum of rotational dynamics from diffusive fluids to arrested solids, thereby resolving inconsistencies in the literature.
This paper establishes a unified theoretical framework, utilizing nonlinear integral equations and Fokker-Planck descriptions with Robin-type boundary conditions, to model surface-mediated autocatalytic processes and identify universal scaling laws that determine whether such systems undergo extinction or explosive growth.
This study employs molecular dynamics simulations to construct and analyze a Nafion thin film on a platinum catalyst surface, revealing that water films thinner than 1.3 nm are stable and demonstrating how hydronium ion crowding and film density influence the interface's electrostatic conditions and differential capacitance.
This paper reports the successful operation and performance evaluation of a 240-pixel transition-edge sensor (TES) spectrometer at SPring-8, demonstrating its high energy resolution and wide-band capabilities for simultaneous multi-element analysis, trace element detection, and XANES studies in fluorescence mode.
This paper introduces the Anisotropic Valence Density Overlap (AVDO) model, a transferable approach for calculating Pauli exchange-repulsion using only two universal parameters that achieves sub-kcal/mol accuracy across diverse organic molecules, thereby offering a promising foundation for high-accuracy, next-generation machine-learned force fields.
This paper presents a method for calculating electric-field-dependent -factors in symmetric top molecules and applies it to RaOCH to identify -doublet levels with small -factor differences, which is crucial for enhancing electron electric dipole moment (eEDM) experiments via laser cooling.
This paper systematically evaluates the i-DMFT method and Collins' conjecture of a linear correlation between correlation energy and entropy, finding that while the approach offers mean-field cost accuracy for certain bond-breaking processes, it fails for heterolytic dissociation, excited states, and complex molecules, thereby establishing specific criteria for the conjecture's validity.
This paper introduces FRAMES, a novel training strategy that leverages minimal temporal information from just two consecutive molecular dynamics frames via an auxiliary loss function to significantly improve the energy and force prediction accuracy of molecular force fields, demonstrating that adding longer trajectory sequences can actually degrade performance.
This paper introduces a highly efficient hybrid ECA-NM optimization method that accurately determines reaction and diffusion parameters for chemical-diffusive models, enabling precise simulation of flame acceleration and deflagration-to-detonation transition with significantly reduced computational cost and error compared to traditional genetic algorithms.