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 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 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 employs set theory and graph theory to model the dissociation micro-states of -protic acids, demonstrating that their graph automorphism group is the direct product of the cyclic group and the symmetric group .
This chapter explores how planetary nebulae, formed by mass ejection from dying low-to-intermediate mass stars, serve as invaluable laboratories for studying astrophysics, astrochemistry, and astromineralogy.
This paper presents a unified framework for calculating electrostatic energy and pressure in periodic Coulomb systems by introducing infinite boundary terms and effective pairwise interactions, which allow the treatment of both neutral and non-neutral systems with point charges and continuous charge distributions using a formulation analogous to isolated systems.
This paper introduces NextHAM, a universal deep learning framework combining a novel E(3)-symmetric Transformer architecture and a zeroth-step Hamiltonian correction strategy, alongside a large-scale benchmark dataset (Materials-HAM-SOC), to achieve highly accurate and efficient prediction of electronic-structure Hamiltonians across diverse materials while explicitly accounting for spin-orbit coupling effects.
Using mixed quantum-classical non-adiabatic dynamics simulations, this study elucidates the microscopic origin of the dual time scales in 3-hydroxychromone's excited-state intramolecular proton transfer by revealing that a competitive out-of-plane hydrogen torsional motion generates a slower pathway alongside the ultrafast canonical proton transfer.
This paper proposes a robust, scalable scheme for deterministically loading single microwave-shielded molecules into optical tweezer arrays with high fidelity and low entropy by utilizing interaction blockade to prevent multiparticle occupancy, thereby enabling large-scale polar molecule arrays for quantum technologies.