ML in Astrophysical Turbulence I: Predicting Prestellar Cores in Magnetized Molecular Clouds using eXtreme Gradient Boosting

Giant Molecular Clouds (GMCs) are dominated by supersonic turbulence and magnetic fields, creating a complex network of shocks and filaments that regulate star formation, making it challenging to predict which individual gas parcels will collapse into star-forming cores. Here, we present an XGBoost machine learning framework to forecast the 3D Lagrangian trajectories and gravitational collapse fate of prestellar gas. Models were trained on about 2.1 million tracer particles from self-gravitating, turbulent magnetohydrodynamic (MHD) simulations. By mapping instantaneous point-wise states (position, velocity, density, and magnetic fields) to future coordinates, our regression model predicts continuous 3D spatial evolution over 0.25 t ff (about 0.31 Myr) with R squared > 0.99. Concurrently, our classifier accurately ranks gravitational core susceptibility (AUC = 0.975). Using SHapley Additive exPlanations (SHAP), we interpret the model's decision hierarchy, revealing that local density, kinetic energy, and magnetic field magnitude dominate core identification. SHAP dependence analysis shows that the model re-derives a sharp non-linear collapse threshold at 10ρ about 0.8 above which magnetic fields amplify collapse predictions. The classification task reveals a conservative false-positive rate (37% precision) at absolute binary thresholds. We demonstrate that while point-wise ML models excel at kinematic advection, they mathematically lack the spatial context required to evaluate local differential operators (such as magnetic directional derivatives, thermal pressure gradients, or field curvature) that are physically necessary to stop collapse. This data-driven approach offers a computationally efficient foundation for advective subgrid modeling while highlighting the necessity of spatial architectures to establish absolute core-formation criteria.