Particle-Guided Diffusion for Gas-Phase Reaction Kinetics
#particle-guided diffusion #gas-phase reactions #reaction kinetics #diffusion modeling #chemical engineering #atmospheric science #simulation
📌 Key Takeaways
- Particle-guided diffusion is a new method for modeling gas-phase reaction kinetics.
- It uses diffusion processes to simulate particle interactions in gas reactions.
- The approach improves accuracy in predicting reaction rates and pathways.
- This technique has potential applications in chemical engineering and atmospheric science.
📖 Full Retelling
arXiv:2603.05139v1 Announce Type: cross
Abstract: Physics-guided sampling with diffusion model priors has shown promise for solving partial differential equation (PDE) governed problems, but applications to chemically meaningful reaction-transport systems remain limited. We apply diffusion-based guided sampling to gas-phase chemical reactions by training on solutions of the advection-reaction-diffusion (ARD) equation across varying parameters. The method generates physically consistent concentr
🏷️ Themes
Chemical Kinetics, Computational Modeling
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Original Source
-- Chemical Physics arXiv:2603.05139 [Submitted on 5 Mar 2026] Title: Particle-Guided Diffusion for Gas-Phase Reaction Kinetics Authors: Andrew Millard , Henrik Pedersen View a PDF of the paper titled Particle-Guided Diffusion for Gas-Phase Reaction Kinetics, by Andrew Millard and 1 other authors View PDF HTML Abstract: Physics-guided sampling with diffusion model priors has shown promise for solving partial differential equation governed problems, but applications to chemically meaningful reaction-transport systems remain limited. We apply diffusion-based guided sampling to gas-phase chemical reactions by training on solutions of the advection-reaction-diffusion equation across varying parameters. The method generates physically consistent concentration fields and accurately predicts outlet concentrations, including at unseen parameter values, demonstrating the potential of diffusion models for inference in reactive transport. Subjects: Chemical Physics (physics.chem-ph) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.05139 [physics.chem-ph] (or arXiv:2603.05139v1 [physics.chem-ph] for this version) https://doi.org/10.48550/arXiv.2603.05139 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Andrew Millard Mr [ view email ] [v1] Thu, 5 Mar 2026 13:09:28 UTC (2,634 KB) Full-text links: Access Paper: View a PDF of the paper titled Particle-Guided Diffusion for Gas-Phase Reaction Kinetics, by Andrew Millard and 1 other authors View PDF HTML TeX Source view license Current browse context: physics.chem-ph < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.LG physics References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers...
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