Physics-Guided Interpretable Machine Learning Framework for Anomalous Transport in Crowded Media with Tunable Flexibility
This paper presents a physics-guided interpretable machine learning framework that combines Brownian Cluster Dynamics simulations with SHAP analysis to quantitatively disentangle the individual and coupled effects of crowding, composition, and tunable bond flexibility on particle transport in crowded media, revealing bond flexibility as a distinct regulator of relaxation in heterogeneous colloidal networks.