Learning force laws in many-body systems

Scientific laws describing natural systems may be more complex than our intuition can handle, thus how we discover laws must change. Machine learning (ML) models can analyze large quantities of data, but their structure should match the underlying physical constraints to provide useful insight. While progress has been made using simulated data where the underlying physics is known, training and validating ML models on experimental data requires fundamentally new approaches. Here we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in dusty plasma, a complex, many-body system. Trained on 3D particle trajectories, the model accounts for inherent symmetries, non-identical particles, and learns the effective non-reciprocal forces between particles with exquisite accuracy (R^2>0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model's accuracy enables precise measurements of particle charge and screening length, discovering violations of common theoretical assumptions. Our ability to identify new physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.
Comment: 14 pages, 4 Figures, 2 Supplemental Figures, 8 Supplemental Videos