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 KinLuv, a comprehensive multistate *ab initio* kinetic modeling framework that explicitly incorporates higher-lying excited states and Herzberg-Teller vibronic coupling to quantitatively reproduce experimental photophysical observables and establish criteria for determining when simplified models are sufficient for designing high-performance organic emitters.
This study utilizes advanced molecular dynamics simulations incorporating temperature-dependent electronic properties to successfully reproduce the pulse-order asymmetry and significantly improve the quantitative accuracy of CO photodesorption probabilities on Pd(111) observed in two-pulse correlation experiments.
This paper extends the MPCC static quantum embedding framework to include triple substitutions via CCSDT solvers and perturbative environment treatments (MPCCSDT(pt) and MPCCSDT(it)), demonstrating that accurate post-CCSD(T) results for challenging systems require not only fragment-level triples but also specific environmental feedback and improved perturbative treatments of single and double amplitudes.
This paper introduces the -CIAH algorithm, a second-order co-iterative augmented Hessian method extended for -point sampling that achieves scaling to deliver Pipek-Mezey Wannier function localization that is 2–3 times faster than first-order -space approaches and orders of magnitude more efficient than -point methods for large systems.
This paper extends the exploitation of molecular symmetry in quantum-chemical calculations from real-character subgroups of to Abelian point groups with complex characters, presenting methods for evaluating integrals and handling block tensors that yield efficiency gains, particularly in systems subjected to finite magnetic fields.
This paper introduces a Markov state model framework that enables the extraction of full path lengths and kinetic properties, such as mean first passage times and rate constants, from the computationally efficient partial paths generated by the replica exchange partial path transition interface sampling (REPPTIS) algorithm.
This paper demonstrates that the Neural Quantum States-based Selected Configuration (NQS-SC) approach significantly outperforms the traditional Variational Monte Carlo (NQS-VMC) method in accuracy, efficiency, and systematic improvability for electronic ground-state calculations, particularly for statically correlated systems, though both methods still struggle with dynamical correlation.
This paper introduces a numerically robust, closed-form effective classical potential based on a mean-field treatment of quantum fluctuations about the path starting point, which accurately estimates quantum thermal expectation values for position-dependent observables in systems with harmonic support.
This study demonstrates that cubic ice, recently made accessible via a gas hydrate precursor, exhibits negative thermal expansion at low temperatures similar to hexagonal ice, while enthalpy calculations confirm its metastable nature and inaccessibility from normal ice Ih.
This paper demonstrates that end-to-end spatiotemporal learning of 2D growth images enables robust early-warning forecasting of compact-to-dendritic transitions in electrodeposition, overcoming the limitations of static descriptors and revealing a low-dimensional latent variable that tracks morphological destabilization.