🔬 Category

physics.chem-ph

1042 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.

From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark

This paper addresses the experimental challenges of analyzing Solid Electrolyte Interphase (SEI) dynamics by introducing the OpIRSpec-7K dataset and benchmark, alongside the Aligned Bi-stream Chemical Constraint (ABCC) framework, which leverages physics-informed flow modeling to accurately predict time-resolved operando infrared spectra from static inputs, thereby enabling interpretable AI-driven electrochemical discovery.

Shuquan Ye, Ben Fei, Hongbin Xu, Jiaying Lin, Wanli Ouyang2026-02-24🤖 cs.AI

Vibronic Landscape of Excitons in Photosynthetic Antenna

This study characterizes the vibrational properties of excitons in purple bacterial light-harvesting proteins to reveal how protein-induced vibronic contributions enhance excitation energy transfer efficiency, contrasting with the mechanism in oxygenic photosynthesis where such contributions above 100 cm⁻¹ are absent.

Manuel J. Llansola-Portoles, James Sturgis, Andrew Gall, Andrew Pascal, Leonas Valkunas, Bruno Robert2026-02-24🔬 physics

Convex Analysis of Relaxation Dynamics in Chemical Reaction Networks and Generalized Gradient Flows

This paper establishes bounds on the Kullback–Leibler divergence to equilibrium for mass-action chemical reaction networks by linking decay rates to stoichiometric singular values and convexity parameters within a generalized gradient flow framework, offering a novel tool to quantify slow relaxation and plateau behaviors in biological systems.

Keisuke Sugie, Dimitri Loutchko, Tetsuya J. Kobayashi2026-02-24🧬 q-bio

The X-ray absorption spectrum of the propargyl radical, C3_3H3⋅_3^{\cdot}

This study combines experimental and computational methods to characterize the near-edge X-ray absorption fine structure (NEXAFS) spectrum of the propargyl radical, identifying a 282.2 eV band arising from carbon 1s to singly occupied molecular orbital transitions with a 420 meV vibrational progression, while also mapping its fragmentation patterns at resonant energies.

Dorothee Schaffner, Theo Juncker von Buchwald, Jacob Pedersen, Andreas Rasp, Emil Karaev, Valentin von Laffert, Alessio Bruno, Michele Alagia, Stefano Stranges, Ingo Fischer, Sonia Coriani2026-02-24🔬 physics

MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry

MACE-POLAR-1 is a new electrostatic foundation model that extends the MACE architecture with explicit long-range interactions and polarisable charge/spin updates, achieving hybrid DFT-level accuracy across diverse chemical systems and significantly improving the prediction of non-covalent interactions and supramolecular complexes.

Ilyes Batatia, William J. Baldwin, Domantas Kuryla, Joseph Hart, Elliott Kasoar, Alin M. Elena, Harry Moore, Mikołaj J. Gawkowski, Benjamin X. Shi, Venkat Kapil, Panagiotis Kourtis, Ioan-Bogdan Magdău (…)2026-02-24🤖 cs.LG

A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy

This study presents a physics-integrated framework combining a physics-regularized neural network and a Kirchhoff-based Markov random field to quantitatively infer internal electrochemical states, such as state-of-charge and ionic conductivity, from operando X-ray spectromicroscopy data of lithium-ion battery cathodes.

Naoki Wada, Yuta Kimura, Masaichiro Mizumaki, Koji Amezawa, Ichiro Akai, Toru Aonishi2026-02-24🔬 cond-mat.mtrl-sci

The interplay of cation/anion and monovalent/divalent selectivity in negatively charged nanopores: local charge inversion and anion leakage

This study demonstrates that the anomalous mole fraction effect and anion leakage in negatively charged wide nanopores are governed by a delicate interplay between charge inversion, anion leakage, and ionic mobility, which can be accurately reproduced by matching the distance of closest approach between ions and surface charges regardless of the specific microscopic model used for surface groups.

Eszter Lakics, Mónika Valiskó, Dirk Gillespie, Dezső Boda2026-02-24🔬 cond-mat.mes-hall

PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment

PackFlow is a generative flow matching framework enhanced by reinforcement learning-based physics alignment that efficiently predicts organic molecular crystal structures by generating lattice-aware proposals which concentrate probability mass in low-energy basins, thereby outperforming heuristic methods in both structural similarity and energy minimization.

Akshay Subramanian, Elton Pan, Juno Nam, Maurice Weiler, Shuhui Qu, Cheol Woo Park, Tommi S. Jaakkola, Elsa Olivetti, Rafael Gomez-Bombarelli2026-02-24🔬 physics