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 introduces a reorganized Hartree-Fock framework that pairs local degrees of freedom with specific solution conditions to enable flexible, efficient imposition of orbital locality while maintaining fast self-consistent field optimization and preserving accuracy in reaction energy predictions.
This paper presents a diagrammatic Monte Carlo method that stochastically samples and resums ladder series contributions to the positron self-energy in molecules, achieving significant memory reduction compared to deterministic Bethe-Salpeter equation solutions while demonstrating quantitative agreement with exact diagonalization benchmarks for lithium hydride.
This paper establishes an exact mapping between near-equilibrium chemical reaction networks and electrical flow problems to design quantum walk algorithms that efficiently solve species reachability, sampling, flux approximation, and Gibbs dissipation estimation, achieving up to quadratic speedups over classical methods through novel multidimensional walk techniques.
This paper presents a validated implementation of the GW-BSE formalism in the CP2K code to accurately predict the optical spectra and excitation sizes of nanographenes, demonstrating its superiority over time-dependent density functional theory for describing electronic excitations in nanostructures.
The paper introduces ELECTRAFI, a fast and differentiable model that predicts periodic charge densities in crystalline materials by leveraging closed-form Fourier transforms of anisotropic Gaussians to achieve state-of-the-art accuracy with up to 633 times faster inference, thereby significantly reducing the total computational cost of DFT calculations.
This paper argues that activity cliffs are largely artifacts of the chosen molecular representation and metric rather than intrinsic molecular properties, demonstrating through a six-step benchmark across fifteen configurations that different embeddings encode distinct aspects of molecular recognition, thereby implicitly defining what constitutes an activity cliff.
This paper identifies a specific quantum resonance in Chesnavich's model for the reaction as a phase-space-localized analogue of classical roaming, characterized by wavefunction concentration between inner and outer transition states and distinct radial and angular momentum signatures.
The paper introduces MLIPilot, an auto-research framework where tool-calling large language models autonomously optimize machine-learned interatomic potentials by proposing code changes and managing HPC jobs under strict physical constraints, successfully transforming initial unstable baselines into production-quality models across diverse molecular and periodic benchmarks.
This study employs classical molecular dynamics simulations to characterize the crystallisation kinetics of supercooled liquid palladium, revealing diffusion-limited growth and a homogeneous nucleation maximum near that aligns with time-resolved X-ray diffraction experiments and indicates that homogeneous nucleation governs the achievable supercooling in rapidly quenched Pd thin films.
This paper presents an exactly solved two-state model for the elastic freely jointed chain that derives explicit analytical expressions for macromolecular stretching behavior, successfully reproducing experimental data for PEG, hyaluronic acid, and DNA transitions while identifying Kuhn length and force constant differences as fundamental driving mechanisms for conformational changes.