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 physics-aware machine learning surrogate for the 3D stochastic Cahn-Hilliard equation that parameterizes inter-cell fluxes to guarantee mass conservation and thermodynamic interpretability, enabling the accurate simulation of noise-driven phenomena like nucleation and coarsening with significant generalization to larger spatial and temporal scales.
By employing interpretable machine learning models on first-principles molecular descriptors, this study reveals that human odor perception lacks a universal physicochemical determinant, instead relying on heterogeneous, odor-specific patterns of feature importance that reflect unique structure-odor relationships for each scent.
This paper investigates how interference effects in dense molecular nanolayers near reflecting surfaces lead to non-monotonic absorption behavior, revealing that while free-standing films are limited to 50% absorption due to symmetry, mirror-backed configurations can achieve unity absorption through critical coupling by balancing radiative leakage with intrinsic loss.
This first-principles study investigates how sodium intercalation into layered potassium birnessite alters its structural stability, ion diffusion barriers, vibrational modes, and electronic properties, revealing that the process induces significant lattice distortions and transforms the material into a tunable bipolar magnetic semiconductor with potential applications in energy storage and spintronics.
This paper explores how concepts from symplectic topology, particularly Gromov's non-squeezing theorem, offer a novel geometric framework for understanding classical reaction dynamics near saddle points by identifying symplectic width scales that reveal how initial phase-space distributions influence reactivity and reaction bottlenecks beyond traditional flux-based metrics.
By combining deep potential molecular dynamics with high-fidelity electronic-structure data, this study resolves decades of uncertainty to precisely locate aluminum's liquid-vapor critical point at approximately 6531–6576 K, 0.637 g/cm³, and 1.6 kbar, establishing a transferable framework for predicting critical phenomena in metals under extreme conditions.
This paper investigates quantum reaction dynamics at an index-1 saddle using a Weyl-symbol formulation of quantum normal forms, revealing that extreme squeezing of transverse bath modes induces a geometric suppression of transmission by depleting effective reactive energy, thereby establishing a concrete link between squeezed-state covariance geometry and quantum reactivity consistent with classical symplectic width concepts.
This theoretical study compares wavepacket interference and impulsive stimulated Raman scattering (ISRS) descriptions of pump-probe spectroscopy, demonstrating that accurate modeling of excited-state absorption requires accounting for non-adjacent vibrational coherences and highlighting the dominant role of the coherent anti-Stokes pathway under specific spectral conditions.
This paper proposes a computational method and algorithm that models molecular cages as constrained graphs to automatically generate substrate-specific structures by connecting binding patterns into the smallest possible molecular paths, capable of constructing cages with over a hundred atoms.
This paper introduces a new algorithm utilizing analytical derivatives within the method of continued fractions to accurately compute eigenvalues for generalized spheroidal wave equations with real and complex parameters, demonstrating its efficacy through applications to quasimolecular systems like , , and .