Machine Learning Prediction of Mechanical Properties in RHEAs

This repository contains machine learning models used to predict key mechanical and electronic properties of refractory high-entropy alloys.

Models Included

1. DOS at Fermi Level (N(Ef))

   • Predicts electronic density of states at the Fermi level using composition-derived descriptors.

2. Young's Modulus (EVRH)

   • Uses N(Ef) and compositional features to estimate Young's modulus.

3. Pugh Ratio (G/B)

   • Predicts ductility indicator using electronic and compositional descriptors.

Workflow

Composition → Descriptor generation → ML model → Property prediction

Author

Dharmendra Pant
Materials Science & Engineering
Clemson University