1032 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 presents an iterative algorithm that utilizes Fourier and Abel transforms along with real-space constraints to successfully restore missing low scattering angle data in anisotropic two-dimensional diffraction patterns of isolated molecules, requiring only approximate knowledge of the molecule's internuclear distances.
This study demonstrates that employing a consistent dipole surface model in both cavity molecular dynamics simulations and spectroscopic post-processing is essential for accurately computing two-dimensional IR-Raman spectra of vibrational polaritons, as inconsistent models severely distort the 2D spectral features despite having minimal impact on linear spectra.
This paper introduces an entropy-corrected zero-temperature approach for finite-temperature density functional theory that utilizes the generalized thermal adiabatic connection formula to explicitly account for exchange-correlation entropy, offering improved accuracy for uniform electron gases at lower densities where standard zero-temperature approximations fail.
This paper demonstrates a novel method for reconstructing the 3D electron density of tumbling proteins by utilizing ion signatures from Coulomb explosions to determine molecular orientations, achieving angular accuracy and resolution comparable to or better than conventional diffraction-only techniques in single-particle X-ray imaging.
This paper presents a theoretical investigation into the electronic structure and properties of radium monochalcogenides (RaO, RaS, RaSe) and RaO ions using advanced relativistic quantum-chemistry methods, revealing that their large dipole moments and non-diagonal Franck-Condon factors arise from the divalent character of their chemical bonding.
By integrating liquid state theory with deep learning, this paper establishes "dielectrocapillarity" as a rigorous first-principles framework demonstrating how electric field gradients enable exquisite, tunable control over the phase transitions, capillary condensation, and adsorption of polar fluids in confined nanoporous environments.
This paper presents a deterministic quantum algorithm that efficiently constructs antisymmetric fermionic states in first quantization mapping using -gates and dirty ancilla qubits, offering a significant performance advantage over sorting-based methods for systems where the particle count is less than the square root of the available orbitals.
This paper establishes a formal link between standard supercell calculations and ab initio polaron equations, demonstrating through quantitative comparisons on TiO2, MgO, and LiF that both methods yield nearly identical polaron wavefunctions and distortions, with minor energy discrepancies attributed to neglected higher-order electron-phonon couplings.
This paper introduces Martini Mapper, an automated framework that generates transferable Martini 3 coarse-grained models directly from SMILES strings for thousands of diverse molecules, thereby overcoming previous manual mapping limitations and enabling high-throughput molecular simulations.
The paper introduces IEPDYN, a continuous-time integral-equation formalism that derives lag-time-free population dynamics from short-timescale molecular dynamics simulations, enabling efficient and accurate prediction of complex binding and unbinding kinetics that are otherwise inaccessible to brute-force methods.