785 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 Perspective reviews a 2023 community challenge where over 70 researchers used diverse computational methods to predict the photochemistry of cyclobutanone and its time-resolved MeV-UED signal, ultimately demonstrating the qualitative predictive power of nonadiabatic molecular dynamics while highlighting the critical impact of electronic-structure theory choices on simulation outcomes.
This paper presents an efficient, scalable implementation of relativistic linear-response coupled-cluster singles and doubles (LR-CCSD) theory that combines X2C-based Hamiltonians, Cholesky decomposition, and perturbation-sensitive natural spinor truncation to accurately compute polarizabilities for large molecular systems, such as Uranium Hexafluoride, while significantly reducing memory and computational costs.
This paper introduces EOM-fpCCSD, a computationally efficient equation-of-motion coupled-cluster method based on a pair coupled-cluster doubles reference that significantly improves the accuracy and convergence of doubly excited and charge-transfer state descriptions compared to standard EOM-CCSD and its pair-tailored variant.
This paper demonstrates that combining Sample-based Quantum Diagonalization (SQD) with Density Matrix Embedding Theory (DMET) enables accurate, chemically precise ground-state energy calculations for complex, low-symmetry ligand-like molecules on IBM's Eagle R3 quantum hardware by effectively managing subsystem-dependent entanglement variations.
This study investigates the fluorescence lifetime response of molecular rotors in ternary PEG mixtures, demonstrating that a linear mixing rule applies to these systems and providing data to critically evaluate the semi-quantitative accuracy of free volume theory in characterizing microviscosity.
This paper introduces a model charge density approach to Ewald summations that accelerates convergence by canceling multipole moments of the crystalline charge distribution, a method validated on gallium arsenide and shown to clarify long-standing implementations in the CRYSTAL code.
The paper introduces El Agente Estructural, a multimodal, natural-language-driven AI agent that mimics human experts to perform precise, context-aware 3D molecular editing and geometry manipulation through the integration of vision-language models and domain-specific tools, thereby enabling complex chemical tasks like site-selective functionalization and stereochemical control without rebuilding entire molecular frameworks.
This paper demonstrates that incorporating quantum-chemical bonding descriptors into machine learning models significantly improves the prediction of elastic, vibrational, and thermodynamic properties of approximately 13,000 solid-state materials while also enabling the discovery of intuitive physical expressions for these properties.
This paper presents a framework for quantifying spin-orbital entanglement in Cr-doped glasses by reconstructing electronic spinors from optical data, revealing a robust linear correlation between the von Neumann entropy and the ratio of spin-orbit coupling to crystal field strength.
UBio-MolFM is a universal molecular foundation model that bridges the gap between quantum-mechanical accuracy and biological scale by leveraging a large bio-specific dataset, an efficient equivariant transformer architecture, and a specialized curriculum learning protocol to achieve ab initio-level fidelity on large biomolecular systems.