Unlocking the composition-property landscape of nickel-based superalloys: a machine learning framework integrating gradient experiments and thermodynamic calculations

This project is a machine learning model for predicting the mechanical properties of nickel-based superalloys. This repository contains pre-trained random forest regression models designed to forecast three key mechanical properties of the alloys. These models were developed using a dataset comprising 4,211 samples. Due to copyright restrictions, only 748 data entries are publicly available. The first 528 entries originate from experimental measurements and publicly accessible literature, while entries 529 through 748 were generated through computational methods.