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.
