785 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 multiscale validation framework for neural ab initio scattering kernels that ensures transport preservation in rarefied binary gas mixtures, demonstrating that a neural surrogate for helium-argon scattering achieves high accuracy in both microscopic scattering metrics and macroscopic DSMC mixture simulations.
This paper establishes a microscopic framework demonstrating that orbital anisotropy in interwoven dual-orbital 2D square lattices generates g-wave altermagnetism, identifying M-TCNX metal-organic framework monolayers as promising candidate materials.
This paper introduces Bipartite Cholesky Graph Networks, a novel architecture that leverages density-fitted Cholesky decomposition to model electron repulsion integrals as a structured bipartite graph, thereby preserving higher-order interaction structures and achieving superior accuracy in predicting molecular correlation energies compared to existing compressed scalar feature approaches.
This paper demonstrates that implementing the thermal Perdew-Burke-Ernzerhof (PBE) functional within Kohn-Sham density functional theory significantly improves the accuracy of warm dense matter simulations—matching path integral Monte Carlo reference data for energies, forces, pressures, and charge densities at negligible computational cost.
This study employs Bayesian inversion of NASA Ames Electric-Arc Shock Tube spectral data to infer and significantly reduce uncertainties in eighteen nitrogen spectroscopic parameters, thereby decreasing the predicted radiative heat flux uncertainty for hypersonic entry by a factor of five.
This paper demonstrates that the formation of chemical bonds, such as in the hydrogen and helium dimers, significantly increases the quantum computational complexity (or "magic") of the electronic ground state, suggesting that regions of strong bonding represent enhanced intrinsic quantum resources.
The paper introduces GENIUS, an agentic AI framework that integrates a Quantum ESPRESSO knowledge graph with a tiered LLM hierarchy and finite-state error recovery to autonomously generate, validate, and repair DFT simulation protocols, thereby democratizing materials discovery by achieving high success rates while significantly reducing costs and hallucinations compared to standard LLM approaches.
This paper introduces EnFlow, a novel energy-guided generative framework that integrates flow-based conformer generation with learned energy landscape modeling to efficiently produce diverse, physically accurate low-energy molecular structures and identify ground states in just one to two sampling steps.
This paper demonstrates that machine-learned force fields, particularly the SO3LR model, significantly outperform conventional molecular mechanics in accurately reproducing quantum-level conformational energetics and vibrational dynamics across diverse biomolecular systems, enabling spectroscopically validated simulations at a fraction of the computational cost.
Drift-React is a novel SE(3)-equivariant generative framework that predicts complete, physically consistent reaction pathways in a single forward pass from reactant and product geometries, eliminating the need for costly iterative force evaluations while achieving state-of-the-art accuracy and orders-of-magnitude speedup for large-scale reaction network exploration.