794 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 demonstrates that transforming molecular Hamiltonians to the adiabatic representation to eliminate spin-orbit coupling inadvertently generates significant non-adiabatic couplings that must be explicitly included to avoid severe errors in rovibronic predictions, providing exact conditions for validity and practical diagnostics for when single-state approximations fail.
This paper introduces AllScAIP, a scalable, attention-based machine-learning interatomic potential that leverages all-to-all node attention to effectively capture long-range interactions and achieve state-of-the-art accuracy across diverse molecular and material systems without relying on explicit physics-based terms.
The paper introduces HEroBM, a deep equivariant graph neural network that utilizes a hierarchical, local-principle-based approach to achieve accurate, transferable, and universal backmapping from coarse-grained to all-atom molecular representations across diverse chemical systems.
This paper introduces a novel, scalable, and size-intensive "Double Configuration Interaction Singles" method that utilizes a perturbative treatment of the electronic Hessian to variationaly account for orbital relaxation, thereby significantly improving the accuracy of charge-transfer excitation energies and single bond dissociation descriptions while maintaining a mean-field computational cost.
This paper investigates the significant coupling between subvalence correlation and higher-order post-CCSD(T) effects on the total atomization energies of first- and second-row molecules, leading to a proposed "W5 theory" protocol that yields revised, highly accurate thermochemical values consistent with Active Thermochemical Tables (ATcT).
This paper introduces a unified variational framework that extends Configuration Interaction Singles (CIS) through orbital optimization, linear-response corrections, and spin-projection techniques to systematically improve the description of both ground and excited states, particularly in strongly correlated systems and bond dissociation scenarios.
This study demonstrates that aqueous chlorine dioxide radicals can serve as transient spin labels for poly(ethylene terephthalate), enabling the identification of plastic types and the measurement of molecular diffusion within the polymer matrix through electron spin resonance spectroscopy.
This paper presents the first full-dimensional quantum scattering calculations for rovibrationally excited HD+HD collisions, identifying near-resonant transitions and low-energy resonances dominated by l=3 partial waves that agree with previous experimental cross sections and provide rate coefficients for temperatures ranging from 0.1 K to 200 K.
This paper presents a theoretical framework demonstrating that the transport and fluorescence dynamics of biexcitons in molecular aggregates are critically governed by the initial state's coherence and momentum composition, revealing distinct transport behaviors for standing versus traveling waves and significant differences between J and H aggregates driven by band structure-dependent interference.
The paper introduces Projected Hessian Learning (PHL), a scalable framework that enables efficient, curvature-informed training of machine-learning interatomic potentials by utilizing stochastic Hessian-vector products instead of explicit Hessian matrices, thereby achieving full-second-order accuracy with significantly reduced computational cost and memory requirements.