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 presents the first full-dimensional quantum scattering calculations for rovibrationally excited HD+HD collisions, identifying near-resonant transitions and low-energy resonances dominated by l=3 partial waves that agree with previous experimental cross sections and provide rate coefficients for temperatures ranging from 0.1 K to 200 K.
This paper presents a theoretical framework demonstrating that the transport and fluorescence dynamics of biexcitons in molecular aggregates are critically governed by the initial state's coherence and momentum composition, revealing distinct transport behaviors for standing versus traveling waves and significant differences between J and H aggregates driven by band structure-dependent interference.
The paper introduces Projected Hessian Learning (PHL), a scalable framework that enables efficient, curvature-informed training of machine-learning interatomic potentials by utilizing stochastic Hessian-vector products instead of explicit Hessian matrices, thereby achieving full-second-order accuracy with significantly reduced computational cost and memory requirements.
This paper elucidates the mechanism behind the improved accuracy of semiclassical dynamics enhanced by generalized quantum master equations by demonstrating that exact "left-handed" time-derivatives delay inaccuracy while introducing long-term instability, and subsequently proposes a protocol to determine memory kernel cutoffs that leverages short-time gains while avoiding unphysical behavior in challenging regimes.
This paper reports the first dipolar carbene-metal-amide material exhibiting enhanced two-photon absorption with a cross-section of 105 GM and efficient thermally activated delayed fluorescence, demonstrating excellent photostability and third-order nonlinear optical properties for advanced photonic applications.
This paper demonstrates that viscosity serves as a reliable indicator for determining the extent of complex formation in concentrated electrolyte solutions, using comparative simulations and experiments on FeCl and MgCl to show that higher viscosity correlates with greater speciation, a finding validated by recent X-ray and neutron scattering data.
This study presents an inverse-design framework combining genetic algorithms with frequency-domain micromagnetics to successfully discover unconventional two-dimensional magnonic crystal structures featuring large band gaps, thereby addressing the challenges of optimizing complex lattice geometries for targeted spin-wave properties.
This paper demonstrates that a particle-guided diffusion model trained on advection-reaction-diffusion solutions can effectively generate physically consistent concentration fields and accurately predict outlet concentrations for gas-phase chemical reactions, even under unseen parameter conditions.
This paper introduces "Ara," an LLM-based agentic workflow that leverages chemical priors to efficiently navigate the design space of covalent organic frameworks, successfully identifying durable and active photocatalysts for solar hydrogen production with significantly higher hit rates and faster convergence than random search or Bayesian optimization.
This paper establishes that projection-based DMRG-in-DFT embedding is inherently nonvariational and fundamentally limited by nonadditive exchange-correlation errors at the subsystem-environment interface, which cannot be resolved even by employing exact functionals or pair-density approaches.