Predicting the mechanical properties of \: Li₂TiO₃ using machine learning process: linear regression, random forests and XGBoost models

Key Points

• Mechanical properties can be accurately predicted using machine learning techniques.
• Key models applied include linear regression, random forests, and XGBoost.
• Assessment involved various predictive modeling techniques on lithium titanium oxide data.
• Highlights the potential for machine learning tools in advancing material property predictions.