Creating the Materials of the Future Using Machine Learning – USC Viterbi | School of Engineering
A new M.S. degree in the Mork Family Department of Chemical Engineering and Materials Science at USC Viterbi will prepare graduates to lead the creation of advanced materials using machine learning and artificial intelligence.
As the world sees rapid advancements in areas such as renewable energy, water management, transport, and quantum computing, the need for new types of advanced materials to support these emerging technologies is increasing. However, developing new materials is laborious—taking decades from concept to implementation.
The manufacturing sector is now turning to artificial intelligence (AI) and machine learning methods for faster materials discovery. But for those looking to pursue a career in this brave new world of materials engineering, there have been few graduate degree options tailored to the integration of materials with machine learning, until now.
To address this significant knowledge gap, the USC Viterbi School of Engineering’s Mork Family Department of Chemical Engineering and Materials Science has launched the new M.S. in Materials Engineering with Machine Learning, a first-of-its-kind Master of Science course, taught by experts in computational materials science and machine learning methods.
Andrea Hodge, Arthur B. Freeman Professor and chair of the Mork Family Department of Chemical Engineering and Materials Science, said that the course reflected an emerging interdisciplinary field that harnessed data and combined modeling and simulations, experiments, and machine learning technologies to create a new approach for materials discovery and manufacturing.
“Currently there are several online courses on materials engineering and courses on many aspects of machine learning, but there is no MS degree program that provides machine learning integration into materials engineering,” Hodge said.
“An increasing number of companies are incorporating a data-driven approach in their business models,” she said. “However, current materials engineering curricula are not addressing the demand due to the lack of integrated machine learning education.”