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 an adaptive, on-the-fly multifidelity machine learning framework that autonomously optimizes training data composition across fidelity levels, significantly reducing data generation costs and eliminating redundancy compared to both single-fidelity and standard multifidelity methods in quantum chemistry applications.
This paper introduces a constrained optimization method using Lagrange multipliers to enforce a target spin-squared expectation value in broken-symmetry DFT calculations, thereby mitigating spin contamination and yielding more consistent and accurate magnetic exchange coupling constants across various systems and functionals.
This study demonstrates that electron transport through the extracellular multiheme cytochrome conduits (MtrF and OmcA) of *Shewanella oneidensis* MR-1 is spin-selective, suggesting that chiral-induced spin selectivity plays a key role in biotic-abiotic electron transfer processes.
This paper generalizes the augmented Roothaan-Hall (ARH) Hessian formalism to spin-restricted open-shell density-functional theory, demonstrating its superior efficiency and robustness in converging challenging electronic states—such as iron-sulfur clusters and singlet excited states—compared to existing optimization methods.
This paper presents a first-principles framework that extends Marcus theory to construct two-dimensional free energy surfaces for coupled ion-electron transfer (CIET) by conditioning diabatic nuclear configurations on interfacial anisotropy, revealing that CO2 reduction kinetics on gold electrodes are governed by saddle-point barriers that differ significantly from traditional one-dimensional treatments.
This paper presents the first implementation of analytical excited-state gradients and derivative couplings within the TDDFT-ris framework, demonstrating that this GPU-accelerated approach with a minimal auxiliary basis set achieves a two- to three-fold speedup over standard TDDFT while maintaining sufficient accuracy for geometry optimizations and emission calculations, despite minor errors in derivative couplings between nearly degenerate states.
Through computer simulations and thermodynamic analysis, this study demonstrates that undersaturated sodium and calcium chloride surface layers on ice form genuine quasi-brine states distinct from bulk three-phase coexistence, which significantly increase premelting thickness while retaining structural and dynamical properties similar to bulk electrolyte solutions.
This paper proposes the concept of a "quantum attomicroscope" capable of imaging sub-femtosecond charge migration dynamics in DNA nucleobase pairs, bridging theoretical simulations with future experimental instrumentation to enable real-time observation and laser-mediated control of quantum chemical reactions in biology.
Using femtosecond stimulated resonance Raman spectroscopy, this study resolves vibrational signatures of three previously elusive dark electronic states in carotenoids, thereby addressing long-standing controversies and providing a refined spectroscopic framework for understanding their roles in photosynthesis.
This paper introduces the Bond Smoothness Characterization Test (BSCT), a computationally efficient metric that detects potential energy surface non-smoothness to both validate Machine Learning Interatomic Potentials and guide iterative architectural improvements, resulting in models that achieve low regression errors while ensuring stable molecular dynamics simulations.