Accelerated Discovery of Multi-Phase Alloys Through Machine Learning Surrogate Models
Team: #13 Samuel Price
Program:
Materials Science and Engineering
Project Description:
In this senior design project, a comprehensive, element-agnostic approach for identifying
promising new alloys is presented. I demonstrate that machine learning algorithms can
be used to create accurate surrogate models of CALPHAD and can rapidly assess the phase stability of
millions of candidate alloys. This approach can greatly expedite the alloy design process.
Team Members
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Project Mentors, Sponsors, and Partners
Johns Hopkins University Whiting School of Engineering
Johns Hopkins University Applied Physics Laboratory
Jonah Erlebacher
Ian McCue
Course Faculty
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