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Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight

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Published on: May 31, 2022

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

Zhengwu Long^1,2, Lingyun You^1,2

¹School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

Journal of Chemical Information and Modeling

|February 9, 2026

Summary

This study reveals asphalt oxidative aging mechanisms using a machine learning potential. It uncovers a sequential reaction network, guiding the design of durable pavement materials.

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Area of Science:

Materials Science
Computational Chemistry
Chemical Engineering

Background:

Atomic-scale mechanisms of asphalt oxidative aging are poorly understood.
Chemical complexity and conventional method limitations hinder asphalt aging research.

Purpose of the Study:

Develop a reactive neural network potential (NNP) for asphalt-oxygen systems.
Enable large-scale molecular dynamics simulations with quantum-mechanical accuracy.
Uncover the reaction network and pathways governing asphalt aging.

Main Methods:

Active learning combined with enhanced sampling (well-tempered metadynamics) to develop a reactive NNP.
Large-scale molecular dynamics simulations using the NNP.
Multimodal experimental characterization.
Free energy analysis of reaction pathways.

Main Results:

A sequential "dehydrogenation-oxidation-crosslinking" reaction network was uncovered.
Thiophene sulfur oxidation initiates aging, followed by hydrogen abstraction, aromatization, and carbonyl formation.
Temperature influences the reaction landscape, favoring carbonylation-aromatization at low temperatures and hydroxylation-aromatization at high temperatures.
Six parallel aging pathways were identified, with sulfoxide and carbonyl channels being dominant.
Aging proceeds via successive polarization of C-H, O-H, C-O, and S-O bonds with lower energy barriers than C-C cleavage.

Conclusions:

A machine learning-accelerated computational framework for asphalt aging was established.
The study provides insights into designing more durable pavement materials.
Understanding the reaction network and energy barriers is crucial for mitigating asphalt aging.