We introduce a data-driven approach to learn generalized collision operators from molecular dynamics. Unlike conventional models (e.g., Landau), the present operator takes a symmetry-breaking form that depends not only on the relative velocity but also on the average velocity of the collision pair, capturing heterogeneous energy transfer arising from collective interactions with the environment. Numerical results show that preserving this overlooked anisotropy is crucial for predicting plasma kinetics and transport coefficients in the moderate coupling regime, where the Landau model fails.

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• Kinetic theory
• Fokker-Planck & Vlasov model
• Machine learning
• Molecular dynamics
• Plasma kinetic theory