This project explores the inverse design of copper alloys using machine learning models to predict alloy compositions that yield desired mechanical properties. The approach leverages a curated dataset of copper-based alloys and implements several generative models (i.e. CVAE, ALAE, CTGAN) to map alloy features to target properties.
- Proof of concept.
- Dataset was modified to replace unknown character with "?"
Gorsse, Stephane; Gouné, Mohamed; LIN, Wei-Chih; Girard, Lionel (2023). Dataset of mechanical properties and electrical conductivity of copper-based alloys. figshare. Dataset. https://doi.org/10.6084/m9.figshare.23735373.v1