794 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 FEMMA, a method that accelerates single-spin quantum optimal control by combining the finite element method to efficiently solve for spin trajectories and gradients with the method of moving asymptotes to achieve rapid convergence toward target fidelities.
This paper uses a self-consistent field-theory framework to demonstrate how hydrodynamic fluctuations in driven electrolytes induce multiple regimes of anomalous diffusion and strong fluctuations, even in the presence of Debye screening.
FragmentFlow is a scalable divide-and-conquer approach that overcomes the challenges of predicting transition states for large molecules by training a generative model to predict the reactive core and then reconstructing the full structure through fragment re-attachment.
The paper introduces NMRTrans, a novel architecture that models NMR spectra as unordered peak sets and leverages a newly curated large-scale experimental dataset (NMRSpec) to achieve state-of-the-art performance in molecular structure elucidation from experimental spectra.
Using molecular simulations and nonequilibrium rate theory, this study demonstrates that field-induced ion pairing dynamics in concentrated electrolytes deviate from classical Onsager theory due to solvent-mediated pathways and dielectric effects, providing a molecular basis for nonlinear conductivity enhancement.
The authors introduce an efficient alchemical free energy protocol using a pretrained, transferable machine-learned potential to calculate the solvation free energies of diverse organic molecules with sub-chemical accuracy.
CheMeleon is a large-scale foundation model that utilizes low-noise classical molecular descriptors for pre-training, enabling message-passing neural networks to outperform traditional machine learning methods and existing foundation models across numerous chemical benchmarks.
This paper extends previous research to provide an explicit, maximal characterization of -representable densities for any number of particles on a one-dimensional torus at finite temperatures, utilizing the convexity of functionals in Sobolev space to accommodate a broad range of potentials and distributions.
This paper presents a machine learning approach using Gaussian process regression and long-range descriptors to predict electron densities in large-scale moiré superlattices, enabling the efficient modeling of complex quantum phenomena that were previously computationally prohibitive for traditional ab initio methods.
This paper utilizes a hybrid nonequilibrium molecular dynamics/Monte Carlo method (H4D) to demonstrate that the linearized Poisson-Boltzmann theory can accurately predict Donnan equilibrium in highly charged slit-pores if renormalized surface charge densities are used, while also showing that explicit solvent effects are minimal in the dilute limit compared to the limitations of charge renormalization.