Reactive Neural Network Potential Developed for Asphalt Aging Systems Through Active Learning and Enhanced Sampling

The atomic-scale mechanisms of asphalt oxidative aging remain poorly understood due to the chemical complexity of asphalt and limitations of conventional methods. Herein, we develop a reactive neural network potential (NNP) for asphalt-oxygen systems via active learning combined with enhanced sampling (well-tempered metadynamics). The NNP achieves quantum-mechanical accuracy while enabling large-scale molecular dynamics simulations. Coupled with multimodal experimental characterization, we uncover a sequential "dehydrogenation-oxidation-crosslinking" reaction network during aging, initiated by thiophene sulfur oxidation and followed by hydrogen abstraction, aromatization, and carbonyl formation. Temperature modulates the reaction landscape, shifting the preference from carbonylation-aromatization at low temperature to hydroxylation-aromatization at high temperature. We identify six parallel pathways with sulfoxide and carbonyl channels being dominant. Free energy analysis reveals that aging proceeds via successive polarization of C-H, O-H, C-O, and S-O bonds with energy barriers significantly lower than C-C cleavage. This work establishes a machine learning-accelerated computational framework for asphalt aging and provides guidance for designing durable pavement materials.