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.

Revealing Light-Driven Dynamics at Nanostructured Solid-Liquid Interfaces with In-Situ SHG

This paper introduces a nanophotonic platform that enhances second harmonic generation by over two orders of magnitude to quantitatively resolve real-time, light-driven interfacial dynamics at solid-liquid interfaces, revealing distinct photocharging and photothermal effects while establishing a unified framework for controlling interfacial charge and potential in energy conversion and catalysis.

Tarique Anwar, Diana DallAglio, Milad Sabzehparvar, Giulia Tagliabue2026-05-04🔬 physics

Non-Equilibrium Dynamics of the Time-Dependent Excitonic Coupling in Fluorescent Protein Dimers

This study quantifies the significantly stronger-than-expected excitonic coupling in dimeric Venus fluorescent proteins by incorporating near-field multipolar effects and resolves the tension between robust coupling and environmental decoherence through a timescale separation mechanism where collective photoexcitation imprints Davydov splitting before rapid environmental dephasing transitions the system to incoherent hopping.

Robson Christie, Cerys Murray, Youngchan Kim, Jaewoo Joo2026-05-04🔬 physics

A Noble-Gas-Centered Coordinate for Within-Period Atomic Property Trends

This paper introduces a single dimensionless, noble-gas-centered coordinate function based on the golden ratio that successfully organizes and predicts key periodic atomic properties—including first ionization energy, electron affinity, electronegativity, and chemical hardness—across multiple periods, accurately reproducing known trends, textbook anomalies, and specific golden-ratio scaling laws with high empirical agreement to NIST data.

Jonathan Washburn, Megan Simons, Elshad Allahyarov2026-05-04🔬 physics

Novel Chemical Pathways for the Formation of Nucleobase Precursors via Benzene {\pi}-Bond Addition to HCN

This paper proposes and computationally validates a novel chemical pathway where HCN undergoes 1,4-cycloaddition to benzene followed by fragmentation to form nucleobase precursors like pyrimidine and purine, suggesting that such organics could have formed during dry phases on early Earth and Mars before being concentrated in aqueous sediments.

Jeehyun Yang, Danica J. Adams, Renyu Hu, Yuk L. Yung2026-05-04🔬 physics

CompleteRXN: Toward Completing Open Chemical Reaction Databases

The paper introduces CompleteRXN, a large-scale supervised benchmark for completing open chemical reaction databases by mapping USPTO records to curated mechanistic reactions, and evaluates various models—including the high-performing Constrained Reaction Balancer (CRB)—to demonstrate that while current methods achieve strong accuracy on controlled splits, significant challenges remain in handling real-world, uncurated data with increasing incompleteness.

Gabriel Vogel, Minouk Noordsij, Evgeny Pidko, Jana M. Weber2026-05-04🤖 cs.LG

Knowing when to trust machine-learned interatomic potentials

The paper introduces PROBE, a post-hoc, architecture-agnostic method that leverages frozen per-atom representations from pretrained machine-learned interatomic potentials to generate reliable per-prediction uncertainty estimates and chemically interpretable diagnostics, outperforming traditional ensemble disagreement approaches while scaling favorably toward foundation-scale models.

Shams Mehdi, Ilkwon Cho, Olexandr Isayev2026-05-04🔬 physics

Experimentally Accurate Graph Neural Network Predictions of Core-Electron Binding Energies

This paper presents an experimentally accurate, interpretable graph neural network model called AugerNet that predicts carbon 1s core-electron binding energies in organic molecules with a mean absolute error of 0.33 eV by leveraging chemically informed node features and E(3)-equivariance to capture local bond environments and generalize to larger systems.

Adam E. A. Fouda, Joshua Zhou, Rodrigo Ferreira, Patrick Phillips, Valay Agarawal, Bhavnesh Jangid, Jacob J. Wardzala, Rui Ding, Junhong Chen, Nicole Tebaldi, Phay J. Ho, Laura Gagliardi, Linda Young2026-05-01🔬 physics