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 paper introduces GODD, a novel diffusion-based framework that leverages an equivariant asymmetric autoencoder to capture distributional structural priors, enabling the generation of valid, unique, and novel 3D molecules in data-scarce regions by training on abundant data and effectively handling structural out-of-distribution shifts.
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 paper presents a novel analog quantum simulation approach for coupled electron-nuclear dynamics in molecules that operates without the Born-Oppenheimer approximation, offering exponential resource savings over classical methods and enabling exact treatment of these dynamics on near-term trapped-ion devices.
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 introduces a comprehensive benchmark dataset comprising 624 high-resolution 2D samples from numerical simulations, designed to facilitate the development and evaluation of machine learning surrogates for modeling pore-scale CO2-water interactions in carbon capture and storage applications.
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 a simulation-based inference framework that integrates physics-based modeling with deep learning to robustly reconstruct quantitative biomolecular folding landscapes and dynamics from minimal single-molecule force spectroscopy data, achieving results comparable to traditional methods while requiring significantly less data and providing built-in uncertainty quantification.
This paper investigates the unexplained "effective body-orderedness" of machine learning interatomic potentials by analyzing the challenges of many-body expansions in ab initio calculations and demonstrating through hydrogen cluster datasets how model types and data composition influence the decomposition of energy, convergence trends, and generalizability.
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.
This study utilizes ab initio many-body perturbation theory to demonstrate that stacking perylene-based molecular crystals with monolayer transition metal dichalcogenides (MoS2 and WS2) enables tunable electronic energy level alignment and the emergence of diverse excitonic states, including hybrid and charge-transfer excitons, thereby establishing organic-inorganic van der Waals heterostructures as a versatile platform for advanced optoelectronic devices.