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Meeting	MS&T22: Materials Science & Technology
Symposium	AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title	Machine Learning Enabled Stacking Fault Energy Prediction in Concentrated Alloys
Author(s)	Dilpuneet S. Aidhy, Gaurav Arora
On-Site Speaker (Planned)	Dilpuneet S. Aidhy
Abstract Scope	High entropy alloys (HEAs) present a paradigm shift in materials design. While these materials present opportunities to unravel novel properties due to a large compositional phase space, they also present an equally large challenge to survey the phase space thereby presenting a data-science challenge. We present a machine learning framework coupled with electronic structure methods whereby properties in complex alloys could be predicted by learning from simpler alloys. A database of charge density is used to predict stacking fault energies in HEAs using regression and neural network models; the latter opens a way to bypass search of descriptors that is a key bottleneck in machine learning methods applied to materials. As the database of simpler materials grows, the self-learning algorithm gradually sharpens its predictive capability and continues to expand into newer material compositions thereby overcoming the challenge of the phase space enormity.

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