997 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 investigates the impact of diffuse interface representations in implicit-solvent models, revealing that while a hyperbolic tangent function with an optimal steepness parameter () improves solvation energy accuracy, binding free energy predictions remain highly sensitive to this parameter, necessitating values between 2 and 20.
This paper presents a unified Fermi's golden rule rate theory for nonadiabatic electron transfer in confined electromagnetic fields that remains valid across all temperature and cavity time scales, extends to lossy cavities via an effective spectral density, and demonstrates key phenomena such as resonance-enhanced rates and electron-transfer-induced photon emission.
This perspective paper reviews the application of Moreau-Yosida regularization in density-functional theory, highlighting its role in reformulating the Kohn-Sham approach, enabling density-potential inversion, and linking the theory to classical field theories, while outlining future development opportunities.
SeQuant is an open-source library that employs a novel graph-theoretic tensor network canonicalizer to unify symbolic and numerical tensor algebra, enabling efficient handling of complex expressions involving symmetries, non-commutative operators, and parametric mode dependencies for applications in quantum simulation and data science.
This paper systematically analyzes the distinct latent feature representations learned by universal machine-learning interatomic potentials (uMLIPs), revealing significant cross-model differences, dataset-dependent trends, persistent pre-training biases after fine-tuning, and a method for compressing atom-level features into global structure-level descriptors.
This paper introduces MechSMILES, a novel framework that trains language models to predict chemical reaction mechanisms via arrow-pushing formalism, thereby enabling physically valid, atom-conserving, and explainable Computer-Assisted Synthesis Planning with high accuracy and rapid adaptability to new reaction classes.
This study employs a machine-learned neuroevolution potential to perform large-scale molecular dynamics simulations, revealing that the thermal conductivity of reduced graphene oxide is strongly suppressed by oxidation but moderately enhanced by higher hydroxyl-to-oxygen ratios, thereby establishing a predictive framework for linking chemical reduction chemistry to heat transport in heterogeneous carbon materials.
This paper presents an adaptive-precision algorithm that leverages 8-bit integer arithmetic on NVIDIA AI accelerators to significantly accelerate density fitting calculations in quantum chemistry, achieving speedups of up to 364% on RTX 6000 Ada GPUs while maintaining the accuracy of standard FP64 methods.
This study demonstrates that dynamical angular fluctuations between ligands in a bis(dipyrrinato)Zn(II) complex break symmetry to transiently enhance excitonic coupling, thereby accelerating inter-ligand exciton transfer.
This study presents a scalable chemical vapor deposition strategy to synthesize twisted graphene layers with pre-designed angles on platinum substrates by leveraging facet-dependent chemical kinetics and substrate reconstruction to control graphene orientation, folding, and tearing.