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.

Chromatographic Peak Shape from Stochastic Model: Analytic Time-Domain Expression in Terms of Physical Parameters and Conditions under which Heterogeneity Reduces Tailing

This paper presents a computationally efficient, time-domain analytic expression for chromatographic peak shapes derived from a stochastic model that integrates axial diffusion, initial variance, and dual retention kinetics, demonstrating superior fitting accuracy over existing methods while challenging the assumption that mechanistic heterogeneity always increases peak tailing.

Hernán R. Sánchez2026-03-30🔬 physics

Pushing the limits of unconstrained machine-learned interatomic potentials

This paper demonstrates that machine-learned interatomic potentials trained on large datasets without strict physical constraints can outperform traditional constrained models in both accuracy and speed, with simple inference-time corrections ensuring their reliability for practical static simulations.

Filippo Bigi, Paolo Pegolo, Arslan Mazitov, Jonathan Schmidt, Michele Ceriotti2026-03-30📊 stat

Third and fourth density and acoustic virial coefficients of neon from first-principles calculations

Using first-principles path-integral Monte Carlo calculations with highly accurate pair and nonadditive many-body potentials, this study determines the third and fourth density and acoustic virial coefficients of neon across a wide temperature range (10–5000 K) with uncertainties significantly smaller than existing experimental data.

Robert Hellmann, Giovanni Garberoglio2026-03-30🔬 physics.atom-ph

KANEL: Kolmogorov-Arnold Network Ensemble Learning Enables Early Hit Enrichment in High-Throughput Virtual Screening

The paper introduces KANEL, an ensemble learning workflow that integrates interpretable Kolmogorov-Arnold Networks with traditional machine learning models and diverse molecular descriptors to enhance early hit enrichment in high-throughput virtual screening.

Pavel Koptev, Nikita Krainov, Konstantin Malkov, Alexander Tropsha2026-03-30🔬 physics

A Priori Sampling of Transition States with Guided Diffusion

This paper introduces ASTRA, a novel method that leverages guided diffusion models and score-aligned ascent to bypass heuristic limitations in transition state search, enabling the high-precision discovery of diverse reaction pathways across complex molecular systems.

Hyukjun Lim, Soojung Yang, Lucas Pinède, Miguel Steiner, Yuanqi Du, Rafael Gómez-Bombarelli2026-03-30🔬 physics

Liquid structure adjacent to solid surfaces follows the superposition principle

This study establishes a universal "solid-liquid superposition" principle and an accompanying analytical model that accurately predicts and explains the complex density distributions and structural oscillations of liquids at heterogeneous solid interfaces across multiple length scales, bridging the gap between experimental observations and theoretical predictions.

Qian Ai, Haiyi Wu, Lalith Krishna Samanth Bonagiri, Kaustubh S. Panse, Shan Zhou, Fujia Zhao, Yitong Li, Kenneth S. Schweizer, Narayana R. Aluru, Yingjie Zhang2026-03-30🔬 physics

Computational Insights into PEMFC Durability: Degradation Mechanisms, Interfacial Chemistry, and the Emerging Role of Machine Learning Potentials

This review synthesizes recent computational advances in understanding the coupled atomistic and molecular degradation mechanisms of PEMFCs, highlighting the limitations of current frameworks in capturing these complex feedback loops and proposing future directions that integrate multiscale modeling with machine learning potentials.

Jack Jon Hinsch, Kazushi Fujimoto2026-03-30🔬 cond-mat.mtrl-sci

Geometric Phase Effect in Thermodynamic Properties and in the Imaginary-Time Multi-Electronic-State Path Integral Formulation

This paper demonstrates that the previously developed imaginary-time multi-electronic-state path integral (MES-PI) formulation naturally captures the geometric phase effect arising from conical intersections, and quantifies its impact on low-temperature thermodynamic properties using an ad hoc GP-excluded construction as a comparison baseline.

Jian Liu2026-03-30✓ Author reviewed ⓘ🔬 physics

The Unreconstructed {\alpha}-Al $_{2}$ O $_{3}$ (0001) Surface is Inhomogeneous and Rough

By combining noncontact atomic force microscopy with density functional theory calculations, this study demonstrates that the unreconstructed $\alpha$ -Al $_{2}$ O $_{3}$ (0001) surface is intrinsically inhomogeneous and rough rather than atomically flat and uniformly Al-terminated as commonly assumed.

Johanna I. Hütner-Reisch, Andrea Conti, David Kugler, Florian Mittendorfer, Michael Schmid, Ulrike Diebold, Jan Balajka2026-03-30🔬 cond-mat.mtrl-sci

Hunting Structural Demons in Digital Reticular Chemistry

This mini-review identifies "structural demons" as chemically invalid models in digital reticular chemistry that compromise computational screening, and proposes a framework to prevent their introduction by integrating synthesis data, standardizing curation, and filtering topology choices early in the workflow.

Yongchul G. Chung, Myoung Soo Lah2026-03-30🔬 cond-mat.mtrl-sci

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