1032 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 hierarchical machine learning framework that distills high-fidelity quantum calculations into coarse-grained Hamiltonians to enable accurate, first-principles prediction of condensed-phase reaction free energies, successfully reproducing experimental proton dissociation constants and enzymatic rates within chemical accuracy.
This paper proposes a primitive framework for chemical dynamics in curved spacetime by revising the nuclear Hamiltonian via a fiducial-observer formulation, demonstrating through numerical simulations that reaction probabilities and spectral bands vanish as spacetime curvature increases while geometric phases remain unaffected.
This perspective article proposes block-encodings and polynomial transformations as a unified, scalable framework for quantum computational science, bridging the gap between abstract algorithmic theory and practical applications in chemistry, physics, and optimization.
This paper introduces an electronic thermostat into the orbital surface hopping framework to resolve artifacts caused by discretizing metallic continua, thereby enabling mixed quantum-classical simulations of molecule-metal interactions that accurately reproduce long-time dynamics and satisfy the principle of detailed balance.
This paper presents the implementation of analytical atomic forces for correlation energy functionals based on the adiabatic-connection fluctuation-dissipation theorem within the random phase approximation (RPA) and RPAx frameworks, demonstrating their high numerical accuracy and systematic improvement over standard DFT methods for predicting geometries, vibrational frequencies, and anharmonic phonon shifts in molecules and solids.
This paper demonstrates that the Gaussian-basis representation of the Superposition of Atomic Potentials (SAP) initial guess can be evaluated through a nearly trivial modification of one-electron nuclear attraction integrals, offering a simpler alternative to the previously proposed two-electron integral approach.
This paper introduces an extended Lagrangian molecular dynamics approach within the nuclear-electronic orbital framework, enhanced by density matrix extrapolation and purification techniques, to efficiently simulate quantum nuclear effects in proton transfer and proton-coupled electron transfer processes across systems ranging from malonaldehyde to large benzimidazole-phenol complexes.
This paper presents a computational study using time-dependent Hartree-Fock theory and a spherical polar pseudospectral representation to analyze the ultrafast laser-driven quantum dynamics of positronium chloride, revealing distinct ionization behaviors and proposing specific photopositron spectral signatures to experimentally distinguish PsCl from positronium.
This paper demonstrates that coating silver nanospheres with thin molecular J-aggregate layers restructures the local electromagnetic vacuum to enable strong coupling and Rabi oscillations in nearby quantum emitters, overcoming the limitations of uncoated nanoparticles where only weak coupling or exponential decay occurs.
This paper proposes a data-efficient statistical framework that quantifies discrepancies in molecular dynamics simulations by measuring distances between covariance matrices, enabling the extraction of low-dimensional features that effectively correlate with global physical properties like diffusion coefficients and distinguish between different phases such as ice and liquid water.