1044 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 HBAT 2, an updated Python package that automates the geometric analysis and visualization of hydrogen bonds and various non-covalent interactions in macromolecular structures through multiple user interfaces.
This study presents a computational-experimental framework combining high-throughput quantum chemistry, data-driven electro-chemical reaction networks, and advanced mass spectrometry to comprehensively characterize the solid-electrolyte interphase (SEI) in lithium-ion batteries, successfully identifying 28 novel SEI species and elucidating their kinetically feasible formation mechanisms to enable rational battery design.
This paper demonstrates that machine learning models for materials discovery often achieve high benchmark performance by exploiting bibliographic confounding factors like author and publication year rather than learning genuine chemical principles, highlighting the urgent need for rigorous falsification tests and dataset redesign to ensure true scientific understanding.
This paper introduces El Agente Sólido, a hierarchical multi-agent framework that leverages large language models to automatically translate natural language objectives into end-to-end solid-state quantum chemistry workflows using Quantum ESPRESSO, thereby lowering expertise barriers and accelerating materials discovery.
This paper introduces El Agente Gráfico, a single-agent framework that enhances scientific automation by embedding LLM decision-making within a type-safe execution environment and dynamic knowledge graphs, thereby replacing fragile text-based coordination with structured, typed symbolic identifiers to ensure consistency, provenance tracking, and robust performance across complex computational tasks like quantum chemistry and materials design.
This study utilizes periodic density functional theory to evaluate the viability of known organic molecular crystals for photocatalytic water splitting and demonstrates that lower-cost gas-phase molecular calculations can effectively screen these materials by accurately predicting their optoelectronic properties.
This paper benchmarks computational methods for predicting the UV-Vis absorption spectra of platinum-based DNA intercalators used in cancer detection, identifying range-separated hybrid functionals as essential for accuracy and establishing PBEh-3c as the most efficient protocol that balances speed with reliable results.
This paper introduces a thermodynamics-informed graph neural network that incorporates the Clapeyron equation as a regularization term to improve the accuracy of multi-task predictions for pure component vapor-liquid equilibrium properties, particularly in data-scarce scenarios.
This study utilizes molecular dynamics simulations to reveal that in high-viscosity lithium-glyme electrolytes, the rotational dynamics of glyme molecules are decoupled from their residence times due to stable polydentate coordination allowing rotation without bond breaking, whereas TFSI anions exhibit a strong correlation between rotation and residence time because their rotation necessitates coordination disruption.
This paper derives formal expressions for reduced 1-particle density matrices and Wigner distributions within the inverse Bogoliubov transformation variant of Thermofield Dynamics by leveraging correlations encoded in the reduced 2-particle density matrix, demonstrating the approach for both harmonic and anharmonic oscillators.