1002 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 presents an active learning workflow that integrates density functional theory, thermochemical modeling, and machine learning to screen over 70 billion candidates, resulting in a generalizable predictive model and the largest public database of CHNO explosives to date, which reveals oxygen balance as the primary driver of detonation performance.
This study presents the first experimental characterization of 316plus (EN 1.4420) stainless steel, revealing that while hydrogen precharging causes a modest strength reduction and significant ductility loss (40–50%) at cryogenic temperatures (77 K and 20 K), the material retains notable ductility (~30% reduction in area), confirming its suitability for liquid hydrogen storage applications.
Experiments simulating Europa-like conditions reveal that while CO2 can be retained in frozen ice and brines via clathrate hydrates or other mechanisms up to 140 K, the resulting infrared spectral signatures do not match those observed by JWST, suggesting that Europa's surface CO2 is unlikely to originate directly from the subsurface ocean without additional processing.
Using leak-out spectroscopy and a validated three-state diabatic model, researchers experimentally demonstrated that the ethynyl radical cation (CCH) exhibits exceptionally strong - mixing driven by Renner-Teller and pseudo-Jahn-Teller effects, where even zero-point vibrational motion is sufficient to disrupt its vibronic structure.
This paper presents a consistently parameterized electrochemical-thermal-degradation model using PyBaMM to predict the capacity fade, state-of-health, and remaining useful life of NMC lithium-ion cells under 81 distinct calendar and cyclic ageing conditions, thereby providing mechanistic insights into the competing effects of solid-electrolyte interphase growth, lithium plating, and active material loss.
This paper critically evaluates Mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT) for photochemical applications, identifying two key limitations—the trade-off between doubly- and singly-excited configurations and the instability caused by abrupt changes in the triplet reference state—and proposes strategies to detect these issues in potential energy surface and nonadiabatic dynamics studies.
This paper presents a novel analytical framework based on a Fredholm integral equation and Padé approximants to derive a compact, explicit expression for transient diffusion-limited currents at disk electrodes, offering a practical and accurate alternative to existing numerical methods for chronoamperometric analysis.
This paper introduces the FB-GNN-MBE framework, which integrates fragment-based graph neural networks with many-body expansion theory to achieve chemically accurate potential energy surface predictions for large molecular systems and demonstrates a transferable teacher-student learning protocol that enables efficient, data-adaptive modeling across diverse water clusters without retraining.
This paper demonstrates that fault-tolerant quantum computers offer a promising solution for simulating complex zero-field nuclear magnetic resonance spectra, which are computationally prohibitive for classical methods, by presenting a holistic analysis from input selection to circuit construction for both small molecules and proteins.
This paper introduces a universal, tree-structured coarse-grained framework enhanced by a Transformer-based propagator that accelerates protein dynamics simulations by 10,000 to 20,000 times while achieving sub-angstrom reconstruction accuracy and generalizing across diverse multi-chain systems.