physics.chem-ph papers | Gist.Science

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.

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

This study demonstrates that machine learning interatomic potentials trained on the chemically diverse OMol25 dataset outperform traditional inorganic-based models in accurately predicting the density, structure, and solvation dynamics of Na-ion battery electrolytes, a finding validated by experimental data and used to reveal how temperature and solvent topology influence ion-solvation behavior.

Nitesh Kumar, Jianwei Lai, Casey S. Mezerkor, Jiaqi Wang, Kamila M. Wiaderek, J. David Bazak, Samuel M. Blau, Ethan J. Crumlin2026-03-23🔬 physics

Faster quantum chemistry simulations on a quantum computer with improved tensor factorization and active volume compilation

This paper presents a novel framework combining block-invariant symmetry-shifted Tensor Hypercontraction (BLISS-THC) and Active Volume compilation for fusion-based photonic quantum hardware, achieving a two-orders-of-magnitude speedup in fault-tolerant quantum chemistry simulations, as demonstrated by benchmarks on the challenging P450 molecule.

Athena Caesura, Cristian L. Cortes, William Pol, Sukin Sim, Mark Steudtner, Gian-Luca R. Anselmetti, Matthias Degroote, Nikolaj Moll, Raffaele Santagati, Michael Streif, Christofer S. Tautermann2026-03-20⚛️ quant-ph

One-Body Properties and Their Perturbative Accuracy with Aufbau Suppressed Coupled Cluster Theory

This paper presents the derivation and implementation of one-body properties within Aufbau Suppressed Coupled Cluster (ASCC) theory, demonstrating that utilizing natural orbitals to achieve starting-orbital independence and preserving perturbative completeness yields dipole moment accuracy comparable to high-level linear response and equation-of-motion coupled cluster methods.

Conor Bready, Harrison Tuckman, Eric Neuscamman2026-03-20🔬 physics

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces

This paper demonstrates that incorporating long-range corrections into machine learning interatomic potentials is essential for achieving robust generalization and transferability across diverse chemical spaces, introducing biased train-test splitting strategies to rigorously benchmark these models on metal-organic frameworks and other materials.

Michal Sanocki, Julija Zavadlav2026-03-20🤖 cs.LG

Spin-Flip Configuration Interaction for Strong Static Correlation in Quantum Electrodynamics

This paper introduces the QED-SF-CIS method, an extension of spin-flip configuration interaction that incorporates quantized cavity photons and a necessary double excitation subspace to accurately model strong static correlation and tunable bond-breaking processes in molecular systems under strong light-matter coupling.

Braden M. Weight, Zheng Pei, Sergei Tretiak2026-03-20🔬 physics

sbml4md: A computational platform for System-Bath Modeling via Molecular Dynamics powered by Machine Learning

The paper introduces **sbml4md**, a machine learning-powered software package that extracts parameters from molecular dynamics trajectories to enable numerically exact, empirically minimal simulations of nonlinear vibrational spectra for molecular liquids using the Hierarchical Equations of Motion framework.

Kwanghee Park, Seiji Ueno, Yoshitaka Tanimura2026-03-20🔬 physics

Isotope Effects in 2D correlation infrared Spectra of Water: HEOM Analysis of Molecular Dynamics-Based Machine Learning Models

This study utilizes a hierarchical equations of motion (HEOM) framework applied to molecular dynamics-based machine learning models to simulate and analyze the non-Markovian, nonlinear vibrational dynamics and isotope effects in the 2D infrared correlation spectra of liquid H2O and D2O.

Kwanghee Park, Ryotaro Hoshino, Yoshitaka Tanimura2026-03-20🔬 physics

Elucidating Norrish Type-I reactive pathways by ultrafast X-ray absorption spectroscopy

By combining ultrafast oxygen K-edge TR-NEXAFS spectroscopy with ab initio multiple spawning simulations, this study elucidates the complete photoexcited population flow in gas-phase acetophenone, revealing a sequential pathway from the initially excited $^1\pi\pi^*$ state to the reactive $^3n\pi^*$ state via intermediate $^1n\pi^*$ and $^3\pi\pi^*$ states that drives Norrish type I chemistry.

Martin Graßl, Pablo Unzueta, Andreas E. Hillers-Bendtsen, Yusong Liu, Diptarka Hait, Alice E. Green, Xinxin Cheng, Felix Allum, Taran Driver, Ruaridh Forbes, James. M. Glownia, Erik Isele, Kirk A. Lar (…)2026-03-20🔬 physics

An SO(3)-equivariant reciprocal-space neural potential for long-range interactions

The paper introduces EquiEwald, a unified SO(3)-equivariant neural interatomic potential that embeds an Ewald-inspired reciprocal-space formulation to accurately model anisotropic long-range electrostatic and polarization interactions while maintaining physical consistency and improving accuracy across periodic and aperiodic systems.

Linfeng Zhang, Taoyong Cui, Dongzhan Zhou, Lei Bai, Sufei Zhang, Luca Rossi, Mao Su, Wanli Ouyang, Pheng-Ann Heng2026-03-20🤖 cs.AI

Maximum entropy distributions of wavefunctions at thermal equilibrium

This paper proposes a maximum entropy principle for quantum wavefunction ensembles at thermal equilibrium, termed the Scrooge ensemble, which demonstrates that valid equilibrium states require constraining measurement entropy to equal the Rényi divergence with respect to the Gibbs state, in addition to standard energy constraints.

Jacob T. Willson, Henrik J. Heelweg, Adam P. Willard2026-03-20🔬 cond-mat

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