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 CHAOS, a large-scale, internally consistent open-access database containing high-accuracy sigma-profiles and diverse quantum-chemical descriptors for over 53,000 molecules, generated via a standardized wB97X-D/def2-TZVP workflow to support advanced molecular modeling and data-driven design across chemistry and materials science.
This paper presents a theoretical model demonstrating that dissolved hydrogen transport, rather than intrinsic catalytic activity, is the primary performance limiter in Liquid Organic Hydrogen Carrier dehydrogenation, revealing distinct kinetic regimes governed by the transition between diffusive release and bubble formation driven by supersaturation and capillarity.
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.
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.
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 presents improved techniques for the transcorrelated density matrix renormalization group (DMRG) method, including optimized matrix product operators, entanglement-aware representations, and parameter tuning, which collectively enable large-scale calculations on lattices and significantly reduce ground-state energy errors compared to standard DMRG.
This paper revisits the Multiconfiguration Self-Consistent Field (MCSCF) method for closed-shell two-electron systems by employing a Newton optimization scheme within a Lagrangian formalism to simultaneously optimize orbitals and configuration coefficients, ultimately reducing the problem to a differential Newton system that is discretized using multiwavelets for iterative solution at the basis set limit.
This study demonstrates that oxygen and fluorine functionalization, combined with cryogenic temperatures, significantly lowers the sulfur sputtering energy threshold in MoS via the formation of volatile products, thereby widening the ion energy window for selective, damage-controlled chalcogen removal in transition metal dichalcogenide plasma processing.
This paper introduces a suite of convolutional neural network classifiers based on the ZeoNet representation that significantly outperform previous methods in distinguishing experimentally synthesized zeolites from computationally predicted hypothetical structures, thereby identifying a small subset of promising candidates for future synthesis.