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 introduces `fix pimd/langevin`, an efficient Path Integral Molecular Dynamics implementation in LAMMPS that leverages the Message Passing Interface to achieve significant computational acceleration and favorable parallel scaling compared to existing tools like i-PI, particularly when used with machine learning interatomic potentials.
This paper introduces the effective Hamiltonian-based TEMPO (EH-TEMPO) algorithm, which reformulates non-Markovian path integral simulations as an imaginary time evolution to achieve significant computational speedups and GPU acceleration while maintaining numerically exact accuracy.
This study characterizes [U-C,N]-butyronitrile as a fully validated 12-spin chain model by combining high-field NMR to extract coupling constants with ultralow-field evolution and indirect -spectroscopy to map the complete spin-spin interaction network, establishing a quantitative benchmark for quantum information and hyperpolarization research.
This paper presents MB-PIPNet, a scalable and efficient monomeric machine-learning potential framework that combines energy decomposition with permutationally invariant polynomial descriptors to accurately model general covalent molecules like linear alkanes while significantly outperforming existing models in computational efficiency.
This study demonstrates that incorporating electronic delocalisation into kinetic Monte Carlo simulations successfully explains the high carrier mobilities and efficient charge generation observed in neat Y6 organic photovoltaics, resolving the mystery of their high performance without large energetic offsets.
This study establishes a quantitatively validated theoretical framework combining TD-DFT, CC2, and Frenkel exciton models to accurately link the structural features of paracyclophanes with their optical and electronic properties, offering design principles for next-generation optoelectronic materials.
This paper proposes a neural network-based scheme that successfully retrieves the initial atomic positions of polyhalomethane isomers from Coulomb explosion imaging data with high precision, enabling automated, event-by-event structure determination for analyzing complex chemical reactions.
This paper demonstrates that mean force matching significantly outperforms other coarse-graining objectives by requiring 50 times fewer training samples and 87% less simulation time while achieving superior accuracy and transferability for unseen proteins, thereby enabling the scalable development of machine-learned coarse-grained models.
This paper demonstrates that the Ishizaki–Tanimura correction in Hierarchical Equations of Motion (HEOM) corresponds to separating smooth and Brownian contributions to the bath's path-integral radius of gyration, and proposes a modified correction alongside an "A4" pole-fitting algorithm to significantly improve the efficiency of HEOM simulations for fast baths and low temperatures.
This paper presents a plane-wave implementation of auxiliary-field quantum Monte Carlo within the PAW formalism in VASP that operates at the complete basis set limit with cubic scaling, achieving high-accuracy predictions of lattice constants and bulk moduli for C, BN, BP, and Si by correcting deficiencies in MP2 and RPA methods.