为零编程基础的合成化学学生设计了可实践的AI入门课
Developing an AI Course for Synthetic Chemistry Students
- 用化学场景替代抽象算法，教学重在上下文理解
- 学生掌握Python、反应优化和分子性质预测等实战能力
- 适合合成化学新生或希望引入AI的课程设计者
人工智能与数据科学正在重塑化学研究，但针对合成与实验化学家的系统性课程仍匮乏，尤其因编程经验不足和缺乏化学实例而难以入门。本文介绍AI4CHEM课程的设计与实施，该课程专为无编程背景的合成化学方向学生打造。课程强调化学语境而非抽象算法，通过免安装的网页平台实现零门槛机器学习流程实践，并结合课堂互动学习。评估方式包括代码引导作业、文献小综述及合作项目，学生需构建解决真实实验问题的AI辅助流程。学习成果显示，学生在使用Python、分子性质预测、反应优化与数据挖掘方面信心显著提升，且具备评估化学AI工具的能力。所有课程材料均公开，提供了一套面向合成化学的、适合初学者的AI教学框架。
原文摘要 · Abstract (English)
Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training.
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