This project uses machine learning to predict the maximum absorption wavelength (λ_max) of organoborazines based on their structural and chemical properties. We employ a Random Forest Regressor model, optimized through hyperparameter tuning.
Organoborazines are an important class of compounds with potential applications in optoelectronic devices. This project aims to accelerate the discovery and design of new organoborazines by predicting their λ_max values using machine learning techniques.
- Data preprocessing and feature scaling
- Random Forest Regression model
- Hyperparameter tuning using GridSearchCV
- Model evaluation using various metrics (MSE, MAE, R2, RMSE, Bias, Correlation Coefficient)