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 extends semi-classical instanton analysis to a symmetric quartic potential with four degenerate minima, deriving energy splittings and Rabi oscillations for distinct tunneling pathways that show excellent agreement with numerical results while revealing a critical coupling regime where the discrete symmetry melts into a continuous symmetry.
This paper introduces an extension of localized intrinsic bond orbitals (IBOs) to decode correlated charge migration dynamics, successfully mapping complex many-electron behaviors to intuitive chemical concepts like curly arrows and hyperconjugation while identifying key mechanisms and efficient molecules through high-level TDDMRG simulations.
This paper presents a computationally efficient theoretical framework based on the Power-Zienau-Woolley Hamiltonian and maximally localized Wannier functions that accurately captures light-matter interactions beyond the electric-dipole approximation in extended systems without requiring finite-order multipole expansions, thereby enabling precise first-principles simulations of spatially structured light dynamics in nanoscale materials.
The paper introduces Geo-ADAPT-VQE, a geometry-aware adaptive variational quantum eigensolver that utilizes natural gradients for operator selection to achieve faster, more stable convergence and significantly shorter ansatz circuits compared to existing gradient-based methods.
This paper presents a scalable, two-step framework that propagates chemical kinetics parameter uncertainties onto reduced manifolds to enable efficient, spatially resolved uncertainty quantification in high-fidelity reacting flow simulations, revealing significant variability in trajectory and equilibrium times driven by mixing and low-to-intermediate temperature chemistry.
This paper derives rigorous quantum mechanical expressions in the electric-dipole approximation that establish generalized Einstein relations between absorption and emission spectra in dispersive media, linking dipole-strength spectra to Einstein coefficients through conditional transition probabilities and defining equilibrium Stokes' shifts via changes in standard chemical potential rather than simple degeneracy ratios.
This paper introduces a universal, first-principles electronegativity scale based on the atomic mean inner potential (AMIP) that not only aligns with established trends and bonding classifications but also outperforms existing methods in predicting Lewis acid strengths and -ray annihilation spectral widths across diverse chemical systems.
This study characterizes the reaction dynamics of O(3P) + O2(3Sigma_g-) collisions on a high-level MRCI+Q/aug-cc-pVQZ potential energy surface, revealing that while the computed rates and isotopic ratios capture experimental trends like negative temperature dependence and cusps, discrepancies in absolute values are primarily attributed to neglected quantum effects such as zero-point energy.
This paper reports the first successful search, observation, and coherent manipulation of electric-quadrupole forbidden vibrational transitions in single trapped nitrogen molecular ions (N) using quantum-logic spectroscopy, thereby overcoming the challenge of complex molecular energy structures to enable new applications in precision spectroscopy, molecular qubits, and infrared clocks.
This paper introduces Variational Adaptive Gaussian Decomposition (VAGD), a scalable, quadrature-free framework that utilizes an autoencoder-decoder neural network to optimize Gaussian wave packet parameters, thereby enabling systematic improvements from thawed Gaussian approximations to full quantum mechanical dynamics through time-sliced semiclassical propagation.