DustNET: enabling machine learning and AI models of dusty plasmas

Abstract

Dusty plasmas are ubiquitous throughout the universe, spanning laboratory and industrial plasmas, fusion devices, planetary environments, cometary comae, and interstellar media. Despite decades of research, many aspects of their behavior remain poorly understood within a unified framework. While numerous theoretical and numerical models describe specific phenomena, such as dust charging, transport, waves, and self-organization, fully predictive models across the wide range of spatial and temporal scales in both laboratory and natural systems remain elusive. Conventional plasma descriptions rely on coupled differential equations for particle densities, momenta, and energies, but their solutions are often limited by computational cost, numerical uncertainties, and incomplete knowledge of boundary conditions and transport processes. Recent advances in machine learning (ML), particularly deep neural networks, offer new opportunities to complement traditional physics-based modeling. Here we review ML and artificial intelligence (AI) approaches, termed bottom-up data-driven methods, for dusty plasma research. Central to this effort is Dust Neural nEtworks Technology (DustNET), a community-driven dataset initiative inspired by ImageNet, integrating experimental, simulation, and synthetic data to enable predictive modeling, uncertainty quantification, and multi-scale analysis. DustNET-trained models may also be deployed in real-time experimental settings under edge computing constraints. Combined with emerging multi-modal AI foundation models and autonomous agents, this framework provides a pathway toward a unified, physics-informed understanding of dusty plasmas across laboratory, industrial, space, and astrophysical environments.

Figures (35)

• Figure 1: Examples of dusty plasma dynamics on different temporal and spatial scales. On the smallest length scale ($L \le r_d$), materials dynamics are important. The dust scale ($L \sim r_d$), based on direct measurements, can be used to examine dust motion using Newton's laws. The largest scale ($L > r_d$) shows rich structures and its evolution, ranging from Coulomb crystals, dust waves to planetary rings, interstellar and intergalactic dust clouds. The thumbnail inserts are from the web searches or scientific literature, such as BoBo:1994Wint:2004TSWS:2021merlino2021dustyGLLW:2022.
• Figure 2: Optical trapping of single particles in atmospheric plasmas, (a) atmospheric argon plasma jet, (b) DC discharge in air, (c) helium DBD in air. The insets show corresponding plasma images.
• Figure 3: Side views of optical trapping of single particles in an RF argon dusty plasma in various locations, (a) the left edge of the electrode, (b) the center of the electrode, and (c) the right edge of the electrode. The trapped particle is a 6.2 µ m glass sphere.
• Figure 4: The first optical trapping experiment in the Auburn MDPX facility. (a) An image of the UOT system that was integrated into the MDPX facility. The upper and lower halves of the cryostat (in black) are visible; (b) A side view of the single particle trapped in the plasma, as marked by the circle; and (c) a top view of the trapped particle in the plasma.
• Figure 5: Transport of a single trapped particle in the plasma from one location to another. Transporting a glass sphere (left-a,b) and a carbon nanotube particle (right-a,b) back and forth in the plasma along the x-axis that is perpendicular to the gravitational field.