Accelerating Materials Design with Machine Learning

Event Sponsor: 
Data Science and Learning Seminar
Start Date: 
Mar 25 2019 - 10:30am
Building 240/Room 1407
Argonne National Laboratory
Logan Ward
Speaker(s) Title: 
The University of Chicago
Ben Blaiszik

The performance of many advanced technologies are inhibited by the slow process of optimizing materials. Physics-based theories and computational tools are proven routes to accelerating materials design but are unavailable or computationally-intractable for many problems. In this talk, we demonstrate how machine learning (ML) can close this capability gap. We start with a discussion of how to use machine learning on different types of materials data, with an emphasis on creating tools to replace costly Density-Functional Theory calculations. We then will describe several case studies – including metallic glass design and radiation damage prediction – to show the advantages and pitfalls of using ML to design materials. Finally, we conclude with an overview of the data infrastructure and software efforts designed to make ML capabilities readily-available to the wider scientific community.

Miscellaneous Information: 

This seminar will be streamed, see details at 

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