High-performance interpretable model for exfoliation energy prediction of 2D nanomaterials

Key Points

• The model achieves high predictive accuracy for exfoliation energy, crucial for material functionality.
• Average R-squared values exceeding 0.90 indicate robust performance across various nanomaterials.
• Analysis employed a machine learning framework, utilizing relevant material properties as inputs for prediction.
• Implications suggest this model could streamline the discovery of new 2D nanomaterials for practical uses.