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 study demonstrates that machine-learned interatomic potentials, particularly through fine-tuned foundation models, effectively overcome the sampling limitations of ab initio methods to accurately model highly concentrated water-in-salt electrolytes over long timescales, while also highlighting the critical impact of reference functional choices on dispersion corrections.
This study establishes high-level quantum chemistry benchmarks for ethylene carbonate adsorption and decomposition on lithium (100) surfaces by extrapolating finite cluster results to the thermodynamic limit, ultimately validating the B97X-V functional as a reliable and affordable alternative to expensive methods for modeling lithium metal anode interfacial chemistry.
This study combines experimental data and atomistic simulations to distinguish between precipitation and surface complexation mechanisms in Mn(II) removal by oilseed rape straw biochar, revealing that high-temperature biochar primarily drives removal through pH-induced alkaline precipitation while lower-temperature variants rely on cation exchange and deprotonated site complexation.
This review article surveys the historical context and recent theoretical, computational, and experimental advances in understanding the microscopic atomic-scale dynamics of liquids, while highlighting future opportunities for applications in energy and biomedicine.
This paper introduces a novel tensorial machine learning model that accurately predicts full solid-state NMR spectra for diverse zeolitic materials and nuclei, overcoming the high computational costs of first-principles calculations and enabling high-throughput simulation for complex crystalline structures.
This paper presents a novel ESIBD-based cryoEM workflow that enables high-resolution structure determination of chemically selected soluble proteins by precisely controlling deposition energy to embed them in vitreous ice, yielding near-atomic resolution maps while revealing how solvent exposure influences dehydration-induced structural rearrangements.
This paper derives analytical nuclear gradients for state-averaged orbital-optimized configuration interaction singles (SACIS) and its spin-projected variant (SAECIS), demonstrating that these low-cost methods accurately reproduce conical intersection geometries and topologies by effectively capturing static correlation through orbital relaxation, thereby offering a reliable black-box alternative to high-level theories at mean-field computational cost.
This paper demonstrates the use of in situ Bayesian machine learning and constrained random walk procedures to design efficient broadband pulsed Dynamic Nuclear Polarization (DNP) pulse sequences directly on spin systems, overcoming the limitations of traditional theoretical approaches for complex electron-nuclear spin interactions.
This paper introduces the positron coupled cluster singles and doubles (POS-CCSD) method to calculate positron binding energies in anions and polyatomic molecules, demonstrating that while quantitative agreement with experiments is currently limited by basis set convergence, the approach successfully highlights the critical role of electron correlation and achieves excellent agreement with high-accuracy theoretical benchmarks for systems like H⁻.
This paper introduces an occupancy extrapolation (OE) method inspired by Landau Fermi liquid theory that efficiently computes accurate valence, Rydberg, and charge-transfer excitation energies at cost by extrapolating from ground-state calculations, thereby avoiding the need for separate SCF computations for each excited state.