Northwestern University

Wed 12:00 PM

WED@NICO SEMINAR: Chris Wolverton, Northwestern University "Using Artificial Intelligence to Discover New Materials"

Chris Wolverton

When: Wednesday, February 13, 2019
12:00 PM - 1:00 PM  

Where: Chambers Hall, Lower Level, 600 Foster St, Evanston, IL 60208 map it

Audience: Faculty/Staff - Student - Public - Post Docs/Docs - Graduate Students

Cost: Free

Contact: Meghan Stagl   847.491.2527

Group: Northwestern Institute on Complex Systems (NICO)

Category: Academic

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Chris Wolverton, Jerome B. Cohen Professor of Materials Science and Engineering, Northwestern University


Using Artificial Intelligence to Discover New Materials


Rational, data-driven materials discovery has the potential to make research and development efforts far faster and cheaper. In such a paradigm, computer models trained to find patterns in massive chemical datasets would rapidly scan compositions and systematically identify attractive candidates. Here, we present several examples of our work on developing machine learning (ML) and deep learning methods capable of creating predictive models using a diverse range of materials data. As input training data, we demonstrate ML on both large computational datasets of DFT calculations, as implemented in the Open Quantum Materials Database (, and also experimental databases of materials properties. We construct ML models using a large and chemically diverse list of attributes, which we demonstrate can be used as an effective tool to automatically learn intuitive design rules, predict diverse properties of crystalline and amorphous materials, such as formation energy, specific volume, band gap energy, and glass-forming ability, and accelerate combinatorial searches.

Speaker Bio:

Chris 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 degree in Physics from the University of Texas at Austin, his PhD degree in Physics from the University of California at 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 and the American Society for Metals. He has published more than 300 papers, with >20,000 citations, and an h-index of 72.

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