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 permutation-invariant, multi-scale full quantum neural network framework that accurately models the joint wavefunction of electrons, nuclei, and muons beyond the Born-Oppenheimer approximation, offering a computationally feasible solution for capturing complex quantum correlations in many-body systems.
This study demonstrates that incorporating quantum nuclear effects into ab-initio density functional calculations significantly improves the agreement between theoretical and experimental electron densities in crystalline LiH and LiD, revealing a breakdown of the strict Born-Oppenheimer approximation and notable temperature dependence in these light-element solids.
This study introduces a non-empirical meta-generalized gradient approximation (meta-GGA) incorporating the Laplacian of the electron density that significantly mitigates self-interaction error in the one-electron system, yielding binding energy curves that outperform existing semilocal functionals like PBE and SCAN and closely match the exact solution.
The paper introduces a new "Pairs and Squares" rendering of the Periodic Table that leverages the fact that the number of orbitals at each atomic energy level forms a perfect square to create a more regular and intuitive structure compared to existing presentations.
This paper introduces DOTA, a deep learning model that leverages local density of states to capture orbital interaction patterns, enabling accurate and efficient prediction of surface adsorption energies by aligning scarce experimental data with multi-fidelity quantum chemistry calculations.
This paper establishes a rigorous mathematical framework for hybrid classical-quantum systems by deriving their microcanonical ensemble via a maximum entropy principle, demonstrating its well-defined nature for continuous energy values and its consistency with the canonical ensemble, while validating the theory through a toy model.
NMIRacle is a novel two-stage generative framework that integrates IR and NMR spectra with count-aware fragment representations to accurately elucidate molecular structures, outperforming existing baselines across varying levels of complexity.
This paper presents the design and characterization of an "ultraslow optical centrifuge" capable of generating linearly polarized fields with arbitrarily low angular acceleration, demonstrating its tunability and successful application in spinning CS molecules for potential use in controlling molecular rotation within viscous media.
This paper introduces a Cholesky-based compression (CBC) algorithm that efficiently applies tree tensor network operators to tree tensor network states, demonstrating runtime performance superior to most established methods while maintaining accuracy comparable to state-of-the-art techniques in both random benchmarks and realistic circuit simulations.
This paper critiques the 2025 i-DMFT method by Di Liu et al. for failing to accurately reproduce the Born-Oppenheimer potential energy curves of hydrogenic halides (HF, HCl, HBr) near equilibrium and for predicting qualitatively incorrect behavior in the long-range Van der Waals region that contradicts multipole expansion theory.