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 study computes and compares the exponential sum- and product-connectivity Gourava indices for benzenoid hydrocarbons, demonstrating that both descriptors strongly correlate with -electronic energies (with ) and that the product-connectivity variant offers a slightly superior fit for high-precision QSPR modeling.
This paper introduces Flux Matching, a framework that extracts dominant reaction pathways and data-driven reaction coordinates directly from reactive trajectory ensembles by learning a current velocity and scalar potential via weighted Helmholtz-Hodge decomposition, thereby enabling mechanism discovery and adaptive sampling without requiring knowledge of underlying dynamics or stationary distributions.
This paper proposes and validates an optimal Langevin Dynamics scheme that significantly reduces sampling bias and improves configurational accuracy for biomolecular simulations, particularly in solvated systems, while maintaining or enhancing computational efficiency and conformational exploration rates.
This paper introduces Dual Vibration Configuration Interaction (DVCI), a memory-efficient computational program that utilizes a novel Hamiltonian factorization based on duality and second quantization to rapidly and precisely calculate specific infrared vibrational states without constructing large matrix blocks.
This study demonstrates controlled chemical signaling between two distinct populations of enzymatic nanomotors, where a glucose-responsive swarm generates a hydrogen peroxide gradient that guides the migration of a secondary catalase-powered swarm, thereby achieving programmable collective behavior through non-reciprocal phoretic interactions.
This Perspective reviews recent advances in Floquet nonadiabatic dynamics methods for closed and open quantum systems, highlights their mechanistic insights into diverse light-matter phenomena, and outlines the key challenges necessary to transition these approaches from model demonstrations to predictive first-principles simulations.
DeltaDiff is a training-free, physics-guided framework that leverages a baseline diffusion model to accurately predict mutant protein structures and capture nonlocal conformational changes without requiring retraining or fine-tuning.
This paper demonstrates that knowledge distillation from a pretrained universal machine-learning interatomic potential, combined with a physics-informed architecture and limited CCSD(T) fine-tuning, enables the creation of data-efficient, quantum-chemical-accuracy potentials for non-covalent interactions by transferring physical priors rather than just labels.
This study demonstrates that supercritical CO2 under non-equilibrium conditions exhibits spontaneous stratification and Brunt-Vaisala oscillations when crossing Widom lines, revealing that the Widom region functions as a dynamic assembly of phase-like behaviors rather than a homogeneous phase.
The paper introduces CLIO, a cognitive agent capable of calibrated deference that successfully guided a closed-loop human-AI campaign to design an improved aqueous organic redox flow battery negolyte by autonomously identifying mechanistic failures and prescribing effective chemical revisions.