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Machine Learning for the Accelerated Design and Discovery of Materials - Virtual

You must apply through the external link. This position is virtual. Please apply by July 11, 2022 for best consideration.

What do batteries, solar cells, wind turbines, and efficient jet engines have in common? They are important to combatting climate change AND their efficacy is limited by the materials available to create them. If you think about it, developing new materials is hard! How many things did ancient humans have to smack, mix together, and burn before they discovered metal? The design space is huge – it’s like finding a needle in a haystack. In 2011, Obama announced the Materials Genome Initiative to develop new materials twice as fast at a fraction of the cost. Humankind’s mastery of materials is so important that we literally named the ages after the materials we could harness. For thousands of years humans advanced through the stone, bronze, and iron ages by trial and error and empirical observations. Then, starting around the 1500s, we started discovering physical equations that describe the laws of nature and used physics and thermodynamics to guide our experimentation. With the advent of computers, we could experiment faster with virtual experiments that explore the performance of molecules and materials with computer simulations. Now, we are entering the “fourth paradigm” of materials science by using big data and machine learning to build predictive models to accelerate materials discovery even further by predicting what material processing steps (or recipes) will lead to desired performance. At NASA Glenn Research Center we are looking for a couple of motivated interns to help us integrate and develop several AI and ML tools into an application that will help scientists find trends in materials data and accelerate materials design and discovery. We are trying to build a metal detector for a needle in the haystack problem. (This project supports: Materials and Structures for Extreme Environments.)

Eligibility requirements:

  • U.S. Citizen
  • Cumulative 3.0 GPA (on a 4.0 scale)
  • Full-time students (high school through graduate)
  • Enrollment in a degree granting institution
  • 16 years of age at the time of application (no exceptions)