Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks
Lagrangian Neural Networks (LNNs) learn a physical system by parameterizing its potential and deriving dynamics from motion data. This study successfully used LNNs to recover the irreducible three-body Axilrod-Teller interaction from particle trajectories. The learned potential accurately reproduced the known radial scaling, angular dependence, and sign reversal of this complex three-body force.