Engineering Mechanical Properties of Tough Elastomers via Machine Learning

This project is ongoing.

Machine Learning (ML) is an innovative paradigm in materials science that enables accurate prediction of properties, rational design, and physical insights across multiple length- and time-scales. This tool is particularly useful to optimize materials that are used under many load-bearing conditions; such as elastomers, hydrogels, and adhesives; because it trains datasets to attain quantitative structure-property relationships in a time-inexpensive manner.

In this project, we will use ML to engineer and optimize the mechanical properties of elastomers. The undergraduate student will develop hard skills in wet chemistry, mechanical testing, and data science to address longstanding problems in polymer physics and fracture mechanics; and soft skills in scientific communication, critical thinking, decision making, and collaboration.


The Office of Undergraduate Research recommends that you attend an info session or advising before contacting faculty members or project contacts about research opportunities. We'll cover the steps to get involved, tips for contacting faculty, funding possibilities, and options for course credit. Once you have attended an Office of Undergraduate Research info session or spoken to an advisor, you can use the "Who to contact" details for this project to get in touch with the project leader and express your interest in getting involved.

Have you tried contacting professors and need more help? Schedule an appointment for additional support.