Introducing Data-Driven Materials Informatics into Undergraduate Courses through a Polymer Science Workshop

With the rapid growth of artificial intelligence and
machine learning
across scientific disciplines from materials discovery to data-driven
problem solving, there is increasing opportunity to integrate these
tools into a broad range of applications. Successful adoption of these
approaches in research can be enhanced by foundational exposure during
undergraduate education. The objective of this study is to introduce
fundamental machine learning concepts to undergraduate students through
a hands-on, application-focused workshop during a polymer science
and engineering course. Students were guided through key steps of
the machine learning workflow, including data cleaning, model training,
performance evaluation, and result interpretation, using a polymer
solubility data set generated via visual inspection. The effectiveness
of the workshop was assessed through pre- and postworkshop student
surveys, which indicated a measurable improvement in students’
understanding and confidence in applying machine learning techniques.
The integration of this workshop into a materials course introduces
the students to the new concepts while extending the application of
the course material.