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 machine-learning framework for coarse-grained molecular dynamics that augments traditional force matching with stochastic Hessian-vector product matching to incorporate second-order curvature information, significantly improving the accuracy and transferability of coarse-grained potentials for biomolecular simulations.
This study employs classical molecular dynamics simulations to investigate helium bubble nucleation, stability, and interfacial tension in liquid lead-lithium alloys across a range of temperatures and compositions, providing critical insights for the design of nuclear fusion reactor breeding blankets.
The paper introduces MPINeuralODE, a novel framework that integrates soft physics-informed residuals with a Multiple-Initial-Condition curriculum to significantly improve the generalization, long-horizon stability, and physical consistency of Neural ODEs across unseen initial conditions.
This paper introduces the concept of "cross-sample prediction churn" to highlight the instability of scientific machine learning models across different training data draws and demonstrates that data-side methods like -bootstrap bagging and the proposed twin-bootstrap approach significantly reduce this churn without sacrificing predictive accuracy, unlike standard parameter-side techniques.
This study leverages machine learning and molecular dynamics to identify potential inhibitors targeting Gram-negative bacterial resistance mechanisms, specifically RND efflux pumps and erythromycin esterases, aiming to overcome the limitations of existing FDA-approved antibacterial drugs.
This review paper examines the progress and potential of quantum computing in advancing quantum chemistry beyond ground state calculations, specifically focusing on applications in reaction mechanisms, dynamics, and finite temperature systems while addressing associated algorithmic challenges and opportunities for experimental impact.
This paper introduces a structure-preserving low-rank compression protocol for two-electron reduced density matrices that reduces memory scaling from quartic to quadratic while maintaining chemical accuracy, thereby enabling efficient application of eigenvector continuation workflows to large-scale nonadiabatic molecular dynamics simulations.
The paper presents X2C-DSRG-MRPT2, an efficient one-step relativistic multireference perturbation theory based on the exact two-component Hamiltonian that accurately captures spin-orbit coupling effects in strongly correlated systems with fifth-power computational scaling and high accuracy.
This study utilizes comprehensive simulations to demonstrate that geometrical imperfections in rectangular wave-driven quadrupole mass filters introduce octupole field distortions that degrade mass resolution and transmission efficiency, with performance further dependent on the initial phase of the applied pulsed waveform relative to the asymmetric rod positions.
This paper introduces a background-free, two-quantum fluorescence-detected pump-probe spectroscopy technique utilizing phase cycling and post-processing to isolate ultrafast exciton-exciton annihilation dynamics and doubly excited electronic states in multichromophoric systems by eliminating incoherent mixing and parasitic signals.