Machine learning for material property prediction
Density Functional Theory based methods for calculating material properties from first principles require large computation facilities and significant computation time. This project aims to develop novel machine learning models and workflows in order to better predict material properties in a fraction of the computation the time that current techniques require.
Student Research Computing Facilitator Profile
Graduate student studying physics with further background in computer science, machine learning, and high performance computing. (Michael Butler, UMaine Orono)