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Learning the diffusion of nanoparticles in liquid phase TEM via physics-informed generative AI
Zain Shabeeb1, Naisargi Goyal1, Pagnaa Attah Nantogmah1
1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA, 30332, USA.
Nature Communications
|July 8, 2025
Summary
We developed LEONARDO, a deep learning model, to analyze nanoparticle motion in liquid environments using advanced microscopy. This method decodes molecular interactions without needing traditional physics equations, revealing complex fluid properties.
Area of Science:
- Nanoscience and nanotechnology
- Soft matter physics
- Machine learning applications in science
Background:
- Studying nanoparticle motion in liquids offers insights into molecular-level interactions.
- Liquid phase transmission electron microscopy (LPTEM) captures real-time nanoparticle dynamics.
- Modeling nanoparticle motion is complex, especially without established physical equations.
Purpose of the Study:
- To present LEONARDO, a novel deep generative model for analyzing nanoparticle motion in LPTEM.
- To overcome challenges in linking observed motion to underlying molecular interactions.
- To interpret interactive forces without relying on closed-form Langevin equations.
Main Methods:
- Utilized a deep generative model with a physics-informed loss function.
- Employed an attention-based transformer architecture to learn stochastic motion.
- Applied the model to nanoparticle trajectories obtained from LPTEM.
Main Results:
- LEONARDO successfully learned the statistical properties of nanoparticle motion.
- The model captured signatures of environmental heterogeneity and viscoelasticity.
- Demonstrated the capability to model complex stochastic processes.
Conclusions:
- LEONARDO provides a powerful new approach for analyzing nanoparticle dynamics in complex fluids.
- The model facilitates a deeper understanding of nanoscale interactions and material properties.
- Physics-informed machine learning can address limitations in traditional modeling techniques.
