Our group works to understand the fundamental behavior of materials at the level of individual atoms. While quantum mechanics can tell us a great deal about how very small numbers of atoms (1-100) interact, the equations are far too slow and complicated to solve to answer interesting questions that involve the interaction of millions or billions of atoms. We use machine learning to connect these quantum mechanical results to models, which can more efficiently predict material behavior and use these to study material properties. Our current focus is on magnetic materials, which includes iron and steel alloys, and shape memory alloys like nitinol.
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