The Purcell Lab focuses on developing and using explainable artificial intelligence (XAI) methods to discover new materials for energy and sustainability applications. We are particularly interested in methods that can be used in the low-data limit, which applies to most materials related problems. Our overall goal is to create integrated frameworks where AI model training and evaluation steps are incorporated into the computational workflows themselves. Students leaving the Purcell group will gain experience in all aspects of our work including: 1) Python and C++ development, 2) Computational chemistry toolkits (electronic structure and molecular dynamics packages), 3) Data visualization techniques, 4) Collaborative software engineering, and 5) Science communication and Open Science principles.
