Data-Driven Techniques to Predict Formation Energy of Binary Materials

In this project the goal was to develop a machine learning model to predict the thermodynamic stability of a material material given simple atom properties. Descriptors for the model were created using the Mendeleev package on data from the AFLOW materials database. The resulting model achieved approximately a .66 accuracy score using a Random Forest regression model.