Projects
What will you be working on?
Projects in this REU are focused on learning concepts in developing technical skills in data science, each of the projects will investigate a unique problem in materials science. Below is an example of a project that students may explore this summer. However, there were many more topics explored last summer in many different way – ranging from designing an instrument for rapidly annealing samples to using machine learning for predicting materials properties or detecting particles in images! Check out posters from last year’s group below!
PROJECTS
Predicting Anisotropy in Lattice Thermal Conductivity
Mentors: Vladan Stevanovic, Prashun Gorai, Emily McDonald
Participant: Henley Sartin
Last summer, Henley worked with researchers in the Stevanovic group to use machine learning to predict anisotropic lattice thermal conductivity, a measure of how well a material is able to transport heat in different directions.
Fingerprinting Disorder in Metallic Alloy Grain Boundaries
Mentors: Jacob Tavenner, Garritt Tucker
Participants: Aaron Schwan, Kim Kusler
Last summer, Aaron and Kim performed atomistic simulations with the Tucker group to learn about disorder at grain boundaries in metal compounds. After successfully learning how to run and analyze these simulations, the pair were able to begin using their dataset for machine learning, where they analyzed the importance of the features they found from their simulations.
Analyzing the Efficacy of the Method of Four Coefficients
Mentors: Caitlin Crawford, Eric Toberer
Participants: Haley Vinton, Erik Bensen
Last summer, Haley and Erik worked together to efficiently solve systems of highly dimensional, non-linear, coupled equations that define the behavior of materials known as thermoelectrics. Haley and Erik’s contributions to this work were included in a recent publication submitted by the Toberer group (currently under review). Congratulations, Haley and Erik!
