This repository contains a sample implementation of using CatBoost for building a surrogate model for transformation temperatures of Shape Memory Alloys (SMAs). The sample code (found in main.py) showcases how CatBoost can be used to model this data and make predictions with a high degree of accuracy. The accompanying raw_data.csv file contains raw data points that can be used to train and validate the model. This data can also serve as a reference for researchers looking to explore the use of CatBoost in this particular field.

To run the sample code, you will need to have CatBoost installed in your environment. The code was tested using version 1.0.6 of CatBoost. If you do not have it installed, you can follow the instructions here to install it. Other necessary packages are: NumPy, pandas, CBFV, and scikit-learn.

• Clone this repository
• Open the main.py file in your preferred Python environment
• Run the code to train and validate the model

If you use this code or the accompanying data set, please cite the original paper. This will ensure proper recognition of the work that has gone into this repository and help further the research in this field.

S. Hossein Zadeh, A. Behbahanian, J. Broucek, M. Fan, G. Vazquez, M. Noroozi, W. Trehern, X. Qian, I. Karaman, R. Arroyave, An interpretable boosting-based predictive model for transformation temperatures of shape memory alloys, Comput. Mater. Sci. 226 (2023) 112225. https://doi.org/10.1016/j.commatsci.2023.112225.