1044 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 demonstrates that first-order phase transitions can manifest as either discontinuous or continuous phenomena depending on whether the number of extensive state variables is less than or greater than or equal to the number of coexisting phases, respectively.
This paper investigates the algebro-geometric properties of the Coupled Cluster Doubles (CCD) truncation variety for four electrons, establishing that it is a complete intersection of a specific degree defined by Pfaffian quadratic relations, and connects these structural findings to the challenging bond formation process of beryllium insertion into molecular hydrogen.
This paper introduces "Enhanced Diffusion Sampling," a framework that combines biased steering protocols with exact reweighting to enable diffusion models to efficiently calculate free energies and sample rare molecular events, thereby overcoming the remaining limitations of equilibrium-only diffusion samplers.
Using photoelectron-photoion coincidence momentum imaging in phase-locked intense laser fields, researchers demonstrated that electron recollisional excitation drives the dissociative ionization of OCS, evidenced by distinct energy-dependent asymmetry flips in electron emission for OCS and S channels that correspond to the parent ion's excitation energy.
The paper introduces MEPIN, a scalable, symmetry-broken equivariant neural network that leverages geometric priors and energy-based objectives to efficiently predict accurate minimum-energy reaction pathways from reactant and product configurations without requiring transition-state data during training.
This study presents a validated C/H/O/N/S chemical network that integrates sulfur kinetics with carbon and nitrogen chemistry through combustion data and ab initio calculations, revealing that C/S coupling significantly alters exoplanet atmospheric abundances and observables, particularly by increasing predicted CS2 levels.
This paper introduces a novel diffusion-based sampling method that enhances exploration of molecular energy landscapes by applying a sequential bias along collective variables, enabling efficient discovery of diverse conformational states, accurate free energy estimation, and reactive sampling with near-first-principles accuracy at a fraction of the computational cost of standard methods.
This paper introduces and benchmarks novel initial guesses for quantum nuclei derived from the three-dimensional quantum harmonic oscillator, demonstrating that their isotropic variant significantly improves the convergence and efficiency of multicomponent mean-field nuclear-electronic orbital calculations compared to existing methods.
This paper introduces a machine learning-enabled high-throughput screening strategy that identifies 27 high-performing solid acid proton conductors from over six million candidates and reveals a universal 2.5 Å oxygen-oxygen distance as the critical mechanistic factor for proton transfer.
Inspired by the Hohenberg-Kohn theorem, this paper proposes an operator-centered machine learning framework that uses the external nuclear potential as input to construct hierarchical representations for efficiently modeling molecular properties and learning operator-to-operator maps, such as those for Fock and density matrices.