Developing an AI Course for Synthetic Chemistry Students
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913017521766400 |
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| author | Zheng, Zhiling |
| author_facet | Zheng, Zhiling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18244 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Developing an AI Course for Synthetic Chemistry Students Zheng, Zhiling Artificial Intelligence Materials Science Physics Education 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. |
| title | Developing an AI Course for Synthetic Chemistry Students |
| topic | Artificial Intelligence Materials Science Physics Education |
| url | https://arxiv.org/abs/2511.18244 |