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README.md

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 A collection of machine learning models for predicting maximum absorption wavelength (λ_max) of chemical compounds in various solvents. These models use molecular descriptors to predict spectroscopic properties, useful for drug discovery, materials science, and computational chemistry applications.
 
+Version: **1.0.0**
+
 ## 🤖 Available Models
 
-| Model | Framework | Architecture | R² Score | MAE (nm) | RMSE (nm) | Status |
-|-------|-----------|--------------|----------|----------|-----------|---------|
-| **AMAX_XGB1** | XGBoost | Gradient Boosting (500 estimators) | 0.9084 | 17.682 | 35.507 | Active |
-| **AMAX_RF1** | Scikit-Learn | Random Forest (500 trees) | 0.9035 | 18.601 | 36.441 | Active |
-| **AMAX_MLP1** | PyTorch | Sequential NN (1024 → 512) | 0.8913 | 23.956 | 38.680 | Active |
+| Model | Architecture | Overall RMSE (nm) | Overall MAE (nm) | Overall R<sup>2</sup> |
+|-----|-----|-----|-----|-----|
+| AMAX_XGB1 | XGBoost | 182.81 | 119.30 | 0.659 |
+| AMAX_MLP1 | PyTorch Sequential MLP | 64.152 | 44.388 | 0.669 |
 
-All models utilize **312 RDKit molecular descriptors** combining both compound and solvent features, trained on a random data split of **32,010 training samples** with **4,001 validation** and **4,002 test samples**. Each model has been retrained to eliminate data leakage and ensure robust performance evaluation.
+All models were evaluated across rigorous scaffold, cluster, and method splits.
 
 ## 📄 Citation