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 introduces a novel RT-TDDFTB+LQBE simulation framework to investigate real-time electron-electron scattering in plasmonic nanoclusters, revealing that quasiparticle lifetimes and relaxation dynamics are highly energy-dependent, exhibit quantum fluctuations in sub-2 nm clusters, and are significantly modulated by interband transitions and Auger scattering in gold.
This paper presents a novel Bayesian inference framework that integrates molecular dynamics simulations and polarisation analysis to overcome the limitations of conventional fitting methods, successfully resolving the previously ambiguous anisotropic rotational motion of liquid benzene and establishing a new paradigm for understanding molecular dynamics in catalysis and energy materials.
This paper introduces Simulated Bifurcation-based Configuration Interaction (SBCI), a classical mechanics-inspired algorithm that significantly reduces the computational cost of high-accuracy electronic structure calculations while maintaining reliability comparable to standard methods.
This paper introduces IR-GeoDiff, a latent diffusion model that recovers three-dimensional molecular geometries from infrared spectra by integrating spectral information into molecular representations, thereby addressing the limitations of existing 2D approaches in capturing the relationship between spectral features and 3D structure.
This paper presents an open-source dynamical box model to simulate global abiotic sulfur cycling on Earth-like planets, revealing that the absence of life would result in marine sediment sulfate concentrations two orders of magnitude higher and sulfide concentrations four orders of magnitude lower than on present-day Earth.
This paper investigates how the characteristics of laser pulses (long versus ultrashort) shape the initial excited molecular state, demonstrating that the exact factorization framework challenges standard Born-Huang concepts like population transfer and vertical excitation by revealing a more complex dependence on the light source.
This paper introduces a scalable quantum-nuclei-corrected NMR crystallography approach (QNC-NMR) that leverages the machine-learning potential PET-MOLS to generate quantum ensembles, thereby significantly improving the accuracy of chemical shielding predictions for hydrogen-bonded protons and enabling applications to amorphous materials without empirical corrections.
This paper presents a theoretical investigation identifying promising vibrational transitions in helical osmocene with significant parity-violating shifts, proposing a pathway for the first experimental detection of parity violation in a chiral molecule using ultra-precise mid-IR spectroscopy.
This paper demonstrates that transforming molecular Hamiltonians to the adiabatic representation to eliminate spin-orbit coupling inadvertently generates significant non-adiabatic couplings that must be explicitly included to avoid severe errors in rovibronic predictions, providing exact conditions for validity and practical diagnostics for when single-state approximations fail.
This paper introduces AllScAIP, a scalable, attention-based machine-learning interatomic potential that leverages all-to-all node attention to effectively capture long-range interactions and achieve state-of-the-art accuracy across diverse molecular and material systems without relying on explicit physics-based terms.