Machine Learning for Discovering New Stable Disordered Materials
Goal
In this project, the trainees will identify material compositions having high propensity to form single-phase structures by training ML models with entropy forming ability (EFA) target functions. Promising candidate compositions will be recommended for experimental synthesis and characterization. Feedback from the experimental results will be used to calibrate the descriptor, particularly to determine the EFA threshold for the formation of high entropy single phases for this class of materials.
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