About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
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Preparing Undergraduate and Graduate Students - and the Faculty who Prepare Them - for Materials Careers (The Judson Education Symposium)
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| Presentation Title |
A Case Study of Teaching Materials Informatics in an Undergraduate Environment With an Industrial No-Code Software Tool |
| Author(s) |
James Edward Saal, Joel Galos |
| On-Site Speaker (Planned) |
James Edward Saal |
| Abstract Scope |
Artificial intelligence (AI) and machine learning (ML) are becoming increasingly important in Materials Science and Engineering research and industry, yet they're largely absent from undergraduate MS&E education. This talk offers a brief overview of current strategies for introducing ML concepts to undergraduate students in materials engineering. While Python-based AI/ML tools exist, their classroom use is limited by their complexity and disconnect from materials. We advocate for materials-centric approaches that use structured data, mirroring industry. A major hurdle is the lack of accessible datasets and tools for non-coders. As a promising solution, Dr. Joel Galos at Cal Poly, SLO, integrated the no-code Citrine Platform into a second-year undergraduate course. Students now explore AI/ML via guided materials case studies, such as predicting corrosion resistance of steels, without programming. This talk encourages discussion on how to effectively integrate AI/ML into undergraduate MS&E curricula and the adoption of tools like the Citrine Platform. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Computational Materials Science & Engineering |