Preliminary Exploration in Polymer Physics Education with AI-based Projects

KONG Xian; SUI Yan

doi:10.14028/j.cnki.1003-3726.2026.25.296

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Preliminary Exploration in Polymer Physics Education with AI-based Projects

更新时间：2026-04-30

- Preliminary Exploration in Polymer Physics Education with AI-based Projects
- Polymer Bulletin Vol. 39, Issue 5, Pages: 799-807(2026)
- 作者机构：
  
  华南理工大学华南软物质科学与技术高等研究院前沿软物质学院　广州 510640
- 作者简介：
- 基金信息：
- DOI：10.14028/j.cnki.1003-3726.2026.25.296
  CLC：
- Received：10 October 2025，
  
  Accepted：22 January 2026，
  
  Online First：26 March 2026，
  
  Published：20 May 2026
- 稿件说明：
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孔宪, 隋岩. 高分子物理的人工智能项目式教学初探. 高分子通报, 2026, 39(5), 799–807.

Kong, X.; Sui, Y. Preliminary exploration in polymer physics education with AI-based projects. Polym. Bull. (in Chinese), 2026, 39(5), 799–807.
孔宪, 隋岩. 高分子物理的人工智能项目式教学初探. 高分子通报, 2026, 39(5), 799–807. DOI： 10.14028/j.cnki.1003-3726.2026.25.296.

Kong, X.; Sui, Y. Preliminary exploration in polymer physics education with AI-based projects. Polym. Bull. (in Chinese), 2026, 39(5), 799–807. DOI： 10.14028/j.cnki.1003-3726.2026.25.296.

摘要

在人工智能变革各个领域的背景下，在高分子物理教学中融入人工智能元素，培养学生数据驱动的意识势在必行，因此本科课程需发展以证据为中心，提升学生的数据意识与跨学科能力。本研究在低年级课程中实施人工智能辅助的软物质理性设计项目式教学，围绕案例、性质、模型与验证，构建分子模拟与机器学习结合的可复现工作流。以聚电解质带电序列效应为案例，学生利用分子模拟获取径向分布函数、回转半径与链末端距等证据，并建立基线模型与深度神经网络完成性质预测与对比。教学结果显示，学生能够完成从建模到评估的闭环，提升证据表达与数据驱动的分析范式。实践表明，该教学模式不仅有助于高分子基本概念的理解和模拟仿真工具的掌握，也有利于数据驱动科研范式的树立。

Abstract

Amid rapid advances in artificial intelligence

integrating AI into polymer physics education is necessary. Undergraduate courses should adopt evidence-centered design to build data literacy and cross-disciplinary skills. We implemented a project-based module on AI-assisted rational design of soft matter in an undergraduate early-stage course. The module is organized around case

properties

model

and validation

and establishes a reproducible workflow that combines molecular simulation with machine learning. Using charge-sequence effects in polyelectrolytes as the case study

students ran simulations to obtain radial distribution functions

radius of gyration

and end-to-end distance. They then trained baseline models and deep neural networks to predict properties and compare methods. The outcomes show that students completed the full loop from modeling to evaluation

improved the clarity of evidence presentation

and adopted a data-driven analytic approach. The module helped students grasp core concepts in polymer physics

gain hands-on experience with simulation tools

and develop habits that support data-driven research.

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references

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