大学化学 >> 2026, Vol. 41 >> Issue (10): 296-308.doi: 10.12461/PKU.DXHX202510010

化学实验 上一篇    

聚乙烯热解的AI辅助ReaxFF动力学虚拟仿真实验

左赵宏1, 邱文豪1, 彭可涵2, 罗皓文2, 王怡涵3, 时允仁1   

  1. 1 重庆大学化学化工学院, 重庆 401331;
    2 重庆大学大数据与软件学院, 重庆 401331;
    3 重庆大学光电工程学院, 重庆 401331
  • 收稿日期:2025-10-09 录用日期:2025-12-19 发布日期:2026-10-13
  • 通讯作者: 左赵宏 E-mail:zzhong@cqu.edu.cn Zhaohong Zuo
  • 基金资助:
    重庆市高等教育教学改革研究重点项目AI+“大物化”课程体系建设探索(252011)；虚拟仿真实验教学一流课程基于ReaxFF模拟计算高聚物降解反应动力学实验；重庆大学校级教改项目AI驱动个人能力提升(2024Y06)；重庆大学人工智能赋能课程建设项目计算化学与现代分子设计；重庆市高等教育教学改革研究项目基于实验室开放构筑大学生“第二课堂”科技创新平台(233010)

AI-assisted ReaxFF kinetic virtual simulation experiment for polyethylene pyrolysis

Zhaohong Zuo1, Wenhao Qiu1, Kehan Peng2, Haowen Luo2, Yihan Wang3, Yunren Shi1   

  1. 1 College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, China;
    2 College of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China;
    3 College of Optoelectronic Engineering, Chongqing University, Chongqing 401331, China
  • Received:2025-10-09 Accepted:2025-12-19 Published:2026-10-13
  • Contact: Zhaohong Zuo E-mail:zzhong@cqu.edu.cn

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摘要： 面向课堂中的机理教学与数据驱动分析训练，本文提出一套标准化、低成本、可复现的聚乙烯热解数字化教学实验。该实验采用反应力场分子动力学方法，在云计算平台完成聚乙烯高温无氧热解过程的分子动力学模拟。为保证计算在课堂环境中可行，选用短链、无支链、无交联的简化聚合物模型。对轨迹数据进行主成分分析和统一流形近似与投影降维，随后在降维嵌入空间上实施聚类分析，结果显示：·H、·CH3、·C2H3等自由基与C2、C3轻质烃为关键物种，机理阶段呈现“氢抽提-β裂解-终止”的演化顺序。实验通过可视化轨迹与降维聚类分析，使热解机理阶段与产物谱的关联清晰可解释，有效服务于机理教学与数据驱动分析训练，显著加深学生对复杂热解过程的理解并培养其跨学科能力，具有良好的教学适用性与推广潜力。

关键词: 数字化实验, 高聚物热解, 聚乙烯, 分子动力学模拟, 主成分分析, 聚类分析

Abstract: To facilitate mechanistic instruction and data-driven analysis training in classroom settings, this study presents a standardized, cost-effective, and reproducible digital teaching experiment for polyethylene (PE) pyrolysis. The experiment employs reactive force field molecular dynamics (ReaxFF MD) simulations conducted on a cloud computing platform to model high-temperature, oxygen-free PE pyrolysis. For computational feasibility in classroom environments, a simplified polymer model featuring short chains without branching or crosslinking was adopted. Trajectory data were analyzed through principal component analysis and uniform manifold approximation and projection for dimensionality reduction, followed by cluster analysis in the reduced embedding space. Results identify key species including ·H, ·CH3, ·C2H3 radicals and C2, C3 light hydrocarbons, revealing a characteristic reaction sequence of “hydrogen abstraction → β-scission → termination”. By integrating visualized trajectories with dimension-reduced clustering analysis, the experiment establishes clear, interpretable correlations between pyrolysis stages and product distributions. This approach effectively bridges mechanistic instruction with data science training, significantly enhancing students’ understanding of complex pyrolysis processes while developing interdisciplinary competencies, demonstrating excellent pedagogical applicability and dissemination potential.

Key words: Digital experiment, Polymer pyrolysis, Polyethylene, Molecular dynamics simulation, Principal component analysis, Cluster analysis