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 perspective paper outlines the necessity of integrating classical simulations, experimental measurements, machine learning, and quantum computing within reproducible, standardized workflows to overcome current limitations and accelerate the reliable discovery of advanced materials.
The paper introduces HamEvo, a fixed-point neural operator that predicts converged Kohn-Sham Hamiltonians with high accuracy and chemical precision across diverse molecular sizes and temperatures, achieving inference speeds up to 242 times faster than conventional density functional theory while enabling access to critical electronic-structure observables.
This paper introduces photomodulated electron energy-loss spectroscopy (EELS) in an optically coupled scanning transmission electron microscope to directly image angstrom-scale photocarrier localization at oxygen-vacancy surface trap states in rhodium-doped strontium titanate nanoparticles, thereby elucidating nanoscale mechanisms that hinder solar water splitting.
This paper evaluates seven fine-tuning strategies for machine-learned interatomic potential (MLIP) foundation models across diverse chemical benchmarks, revealing that while prerequisites like foundation model quality and correct energy initialization are paramount, naive fine-tuning is optimal for single-system accuracy whereas multihead replay uniquely preserves out-of-distribution robustness for broader deployment.
This paper presents a scalable distributed-memory implementation of periodic CCSD theory that enables dense Brillouin zone sampling (up to 216 k-points) to reliably extrapolate cohesive energies and band gaps to the thermodynamic limit, providing definitive benchmark values for eight semiconductors and insulators with errors of approximately 0.1–0.2 eV and 0.4 eV, respectively, compared to experimental data.
This paper presents an impedance model demonstrating that in-phase harmonic oscillations of cell current density and cathode catalyst layer temperature reduce both impedance and static resistivity in PEM fuel cells, primarily through the modulation of the oxygen reduction reaction's exchange current density.
This study uses DFT calculations to demonstrate that the nickel-bis(1,2-dithiolene) nanoribbons found in cable bacteria form stable, tightly stacked structures with sufficient electronic coupling to support efficient charge delocalization, thereby explaining the organism's unusually high centimeter-scale electrical conductivity.
This study extends Löwdin's formalism for nonorthogonal determinants to orbital-optimized density functional calculations within the projector augmented-wave framework, demonstrating that while the method qualitatively reproduces absorption spectra and peak intensities for single-determinant excited states, it struggles with multi-configurational states and shows no systematic improvement from exact exchange or self-interaction corrections.
This paper introduces a quantitative framework based on Nakajima-Zwanzig projection operators to characterize and measure the specific impact of quantum coherence on energy transfer rates, demonstrating its modulating role through the analysis of a dimer system coupled to a structured phonon bath.
This paper investigates the stochastic population dynamics of surface-mediated autocatalytic processes where particles diffuse and undergo competing replication or death events, providing a systematic theoretical analysis of the population's statistical properties across vanishing, steady-state, and exponential growth regimes supported by numerical solutions and Monte Carlo simulations.