1038 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 demonstrates that electric fields can systematically control atom-molecule Feshbach resonances in ultracold sodium-potassium mixtures, revealing distinct trimer bound states and providing a new method for manipulating polyatomic quantum matter.
This paper introduces a novel X2Ccorr scheme that systematically improves the treatment of relativistic two-electron contributions through picture-change corrections, demonstrating its efficacy via benchmark calculations on chalcogen diatomics and complex neodymium aqua-ions.
This paper presents a unified Kähler manifold framework that systematically derives linear response equations for computing electronic excitation energies across various variational models, offering a streamlined alternative to traditional methods like Casida's derivation for Hartree-Fock and DFT.
This study establishes a robust framework for modeling electrified metal-electrolyte interfaces by systematically deriving effective electrode-ion interactions from free-energy profiles, demonstrating that precise force field parameterization and machine-learned potentials are critical for accurately capturing specific ion adsorption effects that significantly alter interfacial properties like the potential of zero charge and differential capacitance.
This study establishes a roadmap for creating transferable Machine-Learning Interatomic Potentials for macromolecular systems by demonstrating that convergence in chemical environments and careful neighbor list construction enable accurate extrapolation from small n-polyalkane training data to larger polymers without prohibitive computational costs.
The AMORE-MD framework utilizes the ISOKANN algorithm and iterative enhanced sampling to automatically identify interpretable, atomistic reaction mechanisms for rare conformational transitions in molecular dynamics without requiring prior knowledge of collective variables or pathways.
MaxwellLink is a modular, open-source Python framework that enables massively parallel, self-consistent simulations of light-matter interactions by seamlessly coupling diverse electromagnetic solvers with various molecular dynamics drivers via a socket-based interface, thereby overcoming traditional scale limitations to explore complex phenomena in spectroscopy, quantum optics, and polaritonics.
This paper introduces the computable information density (CID) as a universal, data compression-based metric that instantaneously quantifies configurational entropy across diverse molecular systems without requiring prior knowledge of structural features, thereby enabling entropy-driven materials design.
This paper benchmarks short-range machine learning potentials for charged metal/electrolyte interfaces, revealing that while models trained on single charge states yield consistent results, those trained on mixed-charge datasets produce inconsistent predictions, thereby highlighting the limitations of local architectures and offering practical guidance for dataset construction in electrochemical simulations.
This paper proposes a data-efficient machine-learning method that incorporates thermal response forces into coarse-grained force fields to overcome thermodynamic state dependence, thereby significantly improving the transferability and predictive accuracy of coarse-grained molecular simulations across different conditions.