1002 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 utilizes NAP-XPS and Raman spectroscopy to demonstrate that CO-based mixtures promote the formation of stable, thin inorganic copper oxide layers on GEM electrodes through mild redox equilibria, thereby offering a mechanism for enhanced long-term detector stability compared to hydrocarbon-based systems.
This paper introduces a coupled-cluster formalism for imaginary-time evolution that converges to standard solutions or provides additional information when they do not exist, while utilizing a newly defined energy variance to identify physically regularized amplitudes in cases where standard solutions are unreasonable.
This study demonstrates that incorporating three-body Axilrod–Teller–Muto interactions into the XDM dispersion model, particularly with the new Z-damping function, yields the most accurate exfoliation energies for layered materials using semi-local density-functional theory to date.
This paper presents a unified framework for expressing cluster and dipolar-cluster symmetry-protected topological states through projector, neural, and tensor-network representations, deriving closed-form weight functions and demonstrating that tensor product states offer a potentially more efficient description for modulated SPT phases than conventional matrix product states.
This paper introduces DD-03B, a massive 40 TB database containing 0.3 billion all-atom dissociation trajectories for over 19,000 ligand-protein complexes, which establishes a foundational resource for training AI models to predict drug-protein kinetics by categorizing interaction mechanisms and providing computed dissociation rates for systems lacking experimental data.
The paper introduces a spin-adapted neural network backflow (SA-NNBF) ansatz that enforces strict spin symmetry through a tensor-compressed sum-of-products spin eigenfunction and particle-hole duality, enabling highly accurate and efficient variational Monte Carlo simulations of strongly correlated systems like the FeMoco cofactor that outperform existing state-of-the-art methods.
This paper presents the implementation of ab initio Hubbard parameter calculation schemes, including a novel linear-response method for energy-dependent parameters, within CP2K's k-point sampling real-time TDDFT program, highlighting the distinct theoretical advantages and dynamical applications of this approach compared to the ACBN0 scheme.
This paper introduces a self-consistent, non-empirical Hessian-level meta-generalized gradient approximation functional (-PBE) that utilizes full density second derivatives to distinguish between atomic and bonding limits, demonstrating accurate chemisorption and molecular properties while highlighting remaining challenges in predicting bulk lattice constants.
This paper proposes a unified machine learning framework integrating Potential-Embedded MACE and Potential-Embedded Electron Density Prediction to simultaneously and accurately simulate atomic forces and electron density distributions across arbitrary electric potentials, enabling large-scale studies of electrochemical interfaces like the Pt(111)/water system.
This paper investigates and establishes the extension procedures, stability conditions, and accuracy metrics for applying extended phase-space symplectic integration to simulate both classical electron dynamics in turbulent magnetic fields and quantum Kohn-Sham time-dependent density-functional theory, thereby paving the way for its broad application across systems with finite and infinite degrees of freedom.