In this episode, Dr. Bryce Meredig and Dr. Wolverton discuss:
- The evolution of Dr. Wolverton’s research and his group’s focus on computational materials modeling and machine learning.
- The challenges and opportunities for computational methods and informatics to accelerate new materials discovery.
- The different methods and tools the Wolverton Group develops to assist in materials research and development.
- Applications of machine learning to materials research.
- The prospects of machine learning and data-driven methods to explain new physics and chemistry.
“I think of Materials Informatics as the application of data-driven tools to solve problems in materials science and engineering. The advent of the field and why we can define it now is because of data.” — Dr. Christopher Wolverton
Dr. Christopher Wolverton is the Jerome B. Cohen Professor of Materials Science and Engineering at Northwestern University. Before joining the faculty, he worked at the Research and Innovation Center at Ford Motor Company, where he was group leader for the Hydrogen Storage and Nanoscale Modeling Group. He received his BS in physics from the University of Texas at Austin, his PhD in physics from the University of California, Berkeley, and performed postdoctoral work at the National Renewable Energy Laboratory (NREL). His research interests include computational studies of a variety of energy-efficient and environmentally friendly materials via first-principles atomistic calculations, high-throughput and machine learning tools to accelerate materials discovery, and “multiscale” methodologies for linking atomistic and microstructural scales. He is a Fellow of the American Physical Society.
Dr. Bryce Meredig, Chief Science Officer and co-founder of Citrine Informatics, researches the application of machine learning to materials science. He earned his PhD in materials science from Northwestern University, where he focused on materials informatics, and his BAS and MBA at Stanford University, where he is also on the faculty of the Department of Materials Science and Engineering. He is the author of more than 20 peer-reviewed publications, including some of the earliest on applying machine learning (ML) to materials development. He was an Arjay Miller Scholar and Terman Fellow at Stanford, and a Presidential Fellow and NDSEG Fellow at Northwestern.
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