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Updated: Jan 17, 2026

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Polymer Microarrays for High Throughput Discovery of Biomaterials
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通过聚合物科学研讨会,将数据驱动材料信息学引入本科课程
Mona Amrihesari1, Blair Brettmann1,2
1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Journal of chemical education
|September 15, 2025
概括
本研究介绍了机器学习 (ML) 概念,通过实践研讨会向本科聚合物科学学生介绍. 研讨会提高了学生在材料科学研究中应用ML技术的理解和信心.
科学领域:
- 材料科学 材料科学 材料科学
- 聚合物工程 聚合物工程
- 计算科学 计算科学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在科学研究中越来越重要,包括材料发现和数据分析.
- 将AI/ML工具集成到研究中需要基本的理解,这可以通过本科教育来培养.
研究的目的:
- 为本科生介绍基本的机器学习概念.
- 通过早期教育,加强在科学研究中采用AI/ML方法.
- 通过实践研讨会将ML整合到聚合物科学和工程课程中.
主要方法:
- 一个实践性的,以应用为重点的研讨会被开发出来,并交付给一个聚合物科学和工程课程的本科生.
- 学生参与完整的机器学习工作流程:数据清理,模型培训,性能评估和结果解释.
- 通过视觉检查生成的聚合物可溶性数据集被用于实际应用.
主要成果:
- 研讨会前后的调查表明,学生对机器学习概念的理解有了可衡量的改善.
- 学生对将机器学习技术应用于材料科学问题的信心显著增加.
- 研讨会成功地将新的计算概念整合到现有的材料科学课程中.
结论:
- 在本科教育中对机器学习的基础接触可以增强其在科学研究中的采用.
- 实践研讨会是一种有效的方法,可以向聚合物科学学生教授机器学习工作流程.
- 将机器学习整合到材料科学课程中,将基本概念与实际研究应用联系起来.
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