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 study demonstrates that a robust nanofabrication protocol restoring atomic-scale purity to epitaxial graphene on SiC enables the emergence of macroscopic excitonic coherence in HMTP overlayers, revealing a Davydov-split vibronic manifold where a dark excitonic branch dominates radiative relaxation via a polaron-mediated pathway.
This paper proposes a rigorous framework that extrapolates molecular dynamics simulation results to the zero time-step limit using a linear thermodynamic model, thereby correcting systematic discretization errors to recover accurate Boltzmann-consistent statistics and simultaneously estimating key thermodynamic properties like heat capacity and compressibility.
This study presents a data-efficient Bayesian optimization framework utilizing low-dimensional, physics-informed molecular descriptors and a reliable inverse mapping scheme to successfully identify optimal chemical structures with targeted entropy and zero-point vibrational energy properties using fewer than 2,000 training data points.
This paper introduces the Enhanced Stokes-Einstein (ESE) model, a hybrid machine learning approach that integrates the Stokes-Einstein equation with molecular SMILES strings to provide strictly physically consistent and highly accurate predictions of infinite-dilution diffusion coefficients in binary liquid systems, outperforming state-of-the-art methods while remaining broadly applicable for process design.
ChemFlow is a novel hierarchical neural network framework that integrates atomic, functional group, and molecular-level features with composition-aware attention mechanisms to accurately predict the physicochemical properties of complex chemical mixtures by modeling multiscale interactions across varying concentrations.
This paper introduces a robust perturbative Super-CI (Super-CIPT) orbital optimization method for two-component CASSCF, demonstrating its superior accuracy and efficiency in treating static correlation and strong spin-orbit coupling for p-block elements compared to conventional one-component approaches.
This paper presents an open-source universal machine learning interatomic potential covering 97 elements, including minor actinides, by integrating a newly constructed heavy element dataset with existing resources to enable advanced materials design for nuclear applications.
This paper presents a physics-informed Graph Neural Network and Transformer-based framework that automates the quantitative analysis of XANES data to determine three-dimensional atomic structures without requiring manual parameterization, thereby facilitating structure-function studies in energy and catalysis.
This paper revives Kutzelnigg's approach of using the squared Dirac operator to solve the four-component Dirac equation within a multiwavelet basis, thereby avoiding negative energy solutions through spectrum folding and achieving high precision validated against analytical and GRASP reference values for one- and two-electron systems.
This paper establishes a connection between information entropy and molecular similarity matrices derived from local atomic environments, proposing entropy as a complexity measure and demonstrating its utility in quantifying molecular similarity and mixing effects through both SMILES-based and SOAP kernel approaches.