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.
By combining noncontact atomic force microscopy with density functional theory calculations, this study demonstrates that the unreconstructed -AlO(0001) surface is intrinsically inhomogeneous and rough rather than atomically flat and uniformly Al-terminated as commonly assumed.
This mini-review identifies "structural demons" as chemically invalid models in digital reticular chemistry that compromise computational screening, and proposes a framework to prevent their introduction by integrating synthesis data, standardizing curation, and filtering topology choices early in the workflow.
This paper demonstrates the control and characterization of isomer populations in singly deuterated proton-bound dimers of dihydrogen phosphate and formate within superfluid helium nanodroplets by utilizing a highly synchronized, tunable dual-oscillator infrared free-electron laser to record hidden infrared spectra of individual isomers.
This paper introduces "split-flows," a novel flow-based framework that treats backmapping as a continuous-time measure transport to enable expressive conditional sampling of atomistic structures and, for the first time, provide a tractable method for computing mapping entropies to quantify information loss in coarse-grained molecular models.
This study develops a DFT-accurate machine-learned potential and utilizes deep potential molecular dynamics with slow quench rates and extended geometry descriptors to successfully generate BO glass models containing over 30% boroxol rings, revealing that the energy-minimized boroxol fraction of 75% closely matches experimental estimates.
This paper introduces rigorous metrics to analyze how unconstrained machine learning models learn physical symmetries, demonstrating that strategically injecting minimal inductive biases can achieve superior stability and accuracy while preserving the scalability of unconstrained architectures.
This study utilizes a deep-learning potential validated by experiments to reveal that polyacrylonitrile facilitates concerted electron-ion transport through a rapid, Li+-coupled sequential ring-formation mechanism triggered by a nucleophile-initiated cyclization rate-limiting step.
This study demonstrates that while quantum effects significantly influence hydrogen permeation through graphdiyne membranes, classical molecular dynamics simulations combined with Feynman-Hibbs corrections can reliably bound these results, provided that the crucial thermal motion of the membrane is included to accurately capture the reduction in permeation barriers.
This paper presents a GPU-accelerated multigrid Gaussian-Plane-Wave density fitting algorithm implemented in PySCF's GPU4PySCF module, which achieves up to 25x speedup over CPU implementations for large-scale Kohn-Sham DFT calculations while maintaining high efficiency for high angular momentum functions.
This study demonstrates a sustainable photocatalytic strategy using microalgae as a sacrificial agent with brookite TiO2 to simultaneously maximize green hydrogen production and convert microalgae into valuable chemicals like methane and carbon monoxide, while also facilitating CO2 capture.