Interpretable neural network discovers new non-reciprocal force laws in dusty plasma
Emory physicists (PNAS, July 2025) trained a physics-structured neural network on 3D particle trajectories from dusty-plasma experiments. It learned the non-reciprocal forces between particles with over 99% accuracy and overturned standard assumptions: particle charge is not simply proportional to radius, and the distance dependence of the forces is not universal. The work won the 2026 PNAS Cozzarelli Prize.
Key facts
- PNAS vol 122 issue 31 (2025); ScienceDaily repost Apr 2026 ('AI just discovered new physics in the fourth state of matter')
- >99% accuracy in describing non-reciprocal interparticle forces
- Corrects long-held assumptions in dusty-plasma theory
- Justin Burton: 'We showed that we can use AI to discover new physics. Our AI method is not a black box.'
Science result
- Field
- physics / plasma physics / soft matter
- Problem
- Inferring many-body force laws in dusty (complex) plasmas
- Result
- Data-driven discovery of non-reciprocal force laws and corrections to standard charge and screening assumptions.
- AI system
- physics-tailored neural network
- Human role
- Human-led with AI tools: experiments and interpretation by physicists
- Verification
- Peer-reviewed in PNAS; Cozzarelli Prize 2026
- Status
- confirmed
What happened
Instead of fitting a pre-assumed force law, the team built physical structure into a neural network and let it learn the interactions from data. The learned laws contradicted textbook assumptions.
Why it matters
It is a clean example of AI discovering new physical laws that humans can interpret, rather than just making predictions.
Changelog
- 2026-09-29: created
Sources (4)
- pressScienceDaily: AI just discovered new physics in the fourth state of matter
- officialEmory News: AI and dusty plasma
- officialEmory: scientists receive Cozzarelli Prize
- paperarXiv 2310.05273 (preprint)
id: 2025-07-30-ai-discovers-dusty-plasma-physics · updated 2026-09-29 · open in the interactive timeline