Fix r2scan model parameter conflicts: Remove mlp_out_bias and version…

README.md

@@ -68,7 +68,7 @@ from chgnet.model.model import CHGNet
 # Load the latest CHGNet model (default: 0.3.0)
 chgnet = CHGNet.load()
 # Load specific CHGNet versions
-chgnet_r2scan = CHGNet.load('r2scan')
+chgnet = CHGNet.load(model_name='r2scan')
 ```
 
 **Model Details:**

chgnet/pretrained/r2scan/README.md

@@ -1,10 +1,10 @@
 ## Model r2SCAN
 
-This is the pretrained weights of CHGNet fine-tuned on the MP-r2SCAN dataset. The model was initialized from the GGA/GGA+U trained CHGNet v0.3.0 and then transferred to the R2SCAN functional dataset. This work is published in the npj Computational Materials paper titled "Cross-functional transferability in foundation machine learning interatomic potentials."
+This is the pretrained weights of CHGNet fine-tuned on the MP-r2SCAN dataset. The model was initialized from the GGA/GGA+U trained CHGNet v0.3.0 and then transferred to the R2SCAN functional dataset. This work is published in the npj Computational Materials paper titled "Cross-functional transferability in foundation machine learning interatomic potentials".
 
 All experiments and results shown in the paper (Method 4) were performed with this version of weights.
 
-Date: 9/15/2025
+Date: 9/21/2025
 
 Author: Xu Huang
 
@@ -44,6 +44,12 @@ model = CHGNet(
 
 MP-r2SCAN dataset (https://doi.org/10.6084/m9.figshare.28245650.v2) with 8-1-1 train-val-test splitting
 
+## Load the Model
+
+```python
+chgnet = CHGNet.load(model_name='r2scan')
+```
+
 ## Training Configuration
 
 We used the pretrained CHGNet v0.3.0 as the starting model and fine-tuned it on the MP-r2SCAN dataset.