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 demonstrates that exploiting both exact and approximate label symmetries enhances the data scaling and generalization efficiency of machine learning models for molecular properties, with a Hessian-based correction effectively mitigating errors when symmetries are not exact.
This paper presents a plane-wave implementation of restricted open-shell Kohn-Sham (ROKS) density functional theory within VASP that enables accurate, spin-pure excited-state calculations with analytical forces for extended systems, demonstrating performance comparable to time-dependent DFT while retaining the favorable scaling of ground-state methods.
This paper presents a unified -scaling framework for second-order perturbation theory by combining block tensor decomposition and canonical polyadic decomposition, which achieves high accuracy for MP2 and rPT2 calculations while reducing storage requirements to .
This paper introduces Domain-Gated Latent Diffusion (DGLD), a novel generative framework that successfully discovers and validates two structurally unique, high-performance energetic materials (L1 and E1) with DFT-confirmed detonation velocities exceeding 8 km/s, overcoming the limitations of existing models that either memorize training data or fail to maintain performance during extrapolation.
This paper presents the derivation, implementation, and benchmarking of analytic first-order nonadiabatic coupling matrix elements for the spin-adapted X-TDDFT method, demonstrating that it significantly reduces errors compared to standard U-TDDFT and provides qualitatively correct insights into the photophysics of open-shell systems like copper(II) porphyrin.
This paper investigates the migration of ortho-phthalate plasticisers in heritage PVC objects to establish that surface cleaning is generally safe and beneficial, while proposing a step-by-step protocol for non-destructive assessment to guide conservation decisions.
The paper introduces FragmentNet, a graph-to-sequence model that employs a novel adaptive tokenizer to decompose molecules into chemically valid fragments of adjustable granularity, demonstrating that pre-training at this fragment level significantly improves downstream property prediction performance compared to traditional atom-level or rigid rule-based approaches.
This study evaluates the photocatalytic oxidation of methane and other pollutants using TiO under UV-C light, presenting a validated model that predicts low conversion efficiencies in ventilation-scale applications but confirms a net climate benefit when COe removal exceeds the energy and material costs of the system.
This paper establishes a two-parameter regime map based on the number of molecules () and excitation number () to delineate the validity of mean-field and single-excitation approximations in collective light-matter dynamics, revealing how excitation density governs the transition from linear harmonic to nonlinear anharmonic Rabi oscillations.
The paper introduces QiankunNet-QSCI, a hybrid quantum-classical framework that combines an efficient unitary selected configuration interaction ansatz executed on the Zuchongzhi 3.1 processor with a transformer neural network to accurately reconstruct electronic wavefunctions and achieve chemical accuracy for strongly correlated systems like the [2Fe-2S] ferredoxin and nitrogenase P-cluster on current noisy intermediate-scale quantum devices.