diff --git a/scripts/train_ntrace.py b/scripts/train_ntrace.py
index 1ead7e112f53e302b18286e41346992c143a9a02..97519d5b3e6d7b35563465128fdf52ea3e7936e2 100644
--- a/scripts/train_ntrace.py
+++ b/scripts/train_ntrace.py
@@ -23,7 +23,7 @@ if __name__ == "__main__":
         "optimizer": "Adam",
         "loss": "composite",
         "cache_dir": "data",
-        "experiment_name": "nTrace_cu_film_6layer",
+        "experiment_name": "nTrace_xx_film_6layer",
         "run_name": "d2048_h32_l6",
         "datadir": "expdata",
     }
diff --git a/src/tgreft/utils/data/data_loader.py b/src/tgreft/utils/data/data_loader.py
index b80df4bb3274e4b942bc93b94f3b7d2f6df15cef..2486aeed3774d0705cec7d1b1565de266b72d815 100644
--- a/src/tgreft/utils/data/data_loader.py
+++ b/src/tgreft/utils/data/data_loader.py
@@ -85,50 +85,26 @@ def generate_data(
     # NOTE: the n-Trace model is more expressive with relaxed bounds
 
     # cu_film, 6 layer
-    parameters_ref = np.column_stack(
-        [
-            np.random.uniform(5.0, 7.0, n_dataset),  # electolyte_sld,
-            np.random.uniform(5, 120, n_dataset),  # electolyte_roughness,
-
-            np.random.uniform(-5.0, 6.5, n_dataset),  # sei_sld,
-            np.random.uniform(10, 500, n_dataset),  # sei_thickness,
-            np.random.uniform(1, 80, n_dataset),  # sei_roughness,
-
-            np.random.uniform(-2, 6, n_dataset),  # material_sld,
-            np.random.uniform(10, 200, n_dataset),  # material_thickness,
-            np.random.uniform(1, 35, n_dataset),  # material_roughness,
-
-            np.random.uniform(6, 7, n_dataset),  # cu_sld,
-            np.random.uniform(20, 700, n_dataset),  # cu_thickness,
-            np.random.uniform(1, 35, n_dataset),  # cu_roughness,
-
-            np.random.uniform(-3.5, 0, n_dataset),  # ti_sld,
-            np.random.uniform(10, 100, n_dataset),  # ti_thickness,
-            np.random.uniform(1, 35, n_dataset),  # ti_roughness,
-
-            np.random.uniform(1, 4.2, n_dataset),  # oxide_sld,
-            np.random.uniform(5, 50, n_dataset),  # oxide_thickness,
-            np.random.uniform(1, 10, n_dataset),  # oxide_roughness,
-        ]
-    )
-
-    # mo_film, 5 layer
     # parameters_ref = np.column_stack(
     #     [
-    #         np.random.uniform(-1.0, 7.0, n_dataset),  # electolyte_sld,  I changed the lower bound to -1 to include air and H2O
+    #         np.random.uniform(5.0, 7.0, n_dataset),  # electolyte_sld,
     #         np.random.uniform(5, 120, n_dataset),  # electolyte_roughness,
 
     #         np.random.uniform(-5.0, 6.5, n_dataset),  # sei_sld,
     #         np.random.uniform(10, 500, n_dataset),  # sei_thickness,
     #         np.random.uniform(1, 80, n_dataset),  # sei_roughness,
 
-    #         np.random.uniform(-2, 7, n_dataset),  # bulk_3_sld,
+    #         np.random.uniform(-2, 6, n_dataset),  # material_sld,
     #         np.random.uniform(10, 200, n_dataset),  # material_thickness,
-    #         np.random.uniform(1, 55, n_dataset),  # material_roughness,
-            
-    #         np.random.uniform(2, 6, n_dataset),  # bulk_2_sld,
+    #         np.random.uniform(1, 35, n_dataset),  # material_roughness,
+
+    #         np.random.uniform(6, 7, n_dataset),  # cu_sld,
     #         np.random.uniform(20, 700, n_dataset),  # cu_thickness,
-    #         np.random.uniform(1, 55, n_dataset),  # cu_roughness,
+    #         np.random.uniform(1, 35, n_dataset),  # cu_roughness,
+
+    #         np.random.uniform(-3.5, 0, n_dataset),  # ti_sld,
+    #         np.random.uniform(10, 100, n_dataset),  # ti_thickness,
+    #         np.random.uniform(1, 35, n_dataset),  # ti_roughness,
 
     #         np.random.uniform(1, 4.2, n_dataset),  # oxide_sld,
     #         np.random.uniform(5, 50, n_dataset),  # oxide_thickness,
@@ -136,10 +112,10 @@ def generate_data(
     #     ]
     # )
 
-    # extended 6 layer model
+    # mo_film, 5 layer
     # parameters_ref = np.column_stack(
     #     [
-    #         np.random.uniform(-1.0, 7.0, n_dataset),  # electolyte_sld, including air and H2O
+    #         np.random.uniform(-1.0, 7.0, n_dataset),  # electolyte_sld,  I changed the lower bound to -1 to include air and H2O
     #         np.random.uniform(5, 120, n_dataset),  # electolyte_roughness,
 
     #         np.random.uniform(-5.0, 6.5, n_dataset),  # sei_sld,
@@ -147,23 +123,42 @@ def generate_data(
     #         np.random.uniform(1, 80, n_dataset),  # sei_roughness,
 
     #         np.random.uniform(-2, 7, n_dataset),  # bulk_3_sld,
-    #         np.random.uniform(10, 200, n_dataset),  # bulk_3_thickness,
-    #         np.random.uniform(1, 55, n_dataset),  # bulk_3_roughness,
-            
-    #         np.random.uniform(2, 7, n_dataset),  # bulk_2_sld,
-    #         np.random.uniform(20, 700, n_dataset),  # bulk_2_thickness (cu_thickness),
-    #         np.random.uniform(1, 55, n_dataset),  # bulk_2_roughness (cu_roughness),
-            
-    #         np.random.uniform(-3.5, 7, n_dataset),  # bulk_1_sld,
-    #         np.random.uniform(10, 200, n_dataset),  # bulk_1_thickness,
-    #         np.random.uniform(1, 55, n_dataset),  # bulk_1_roughness,
-            
+    #         np.random.uniform(10, 200, n_dataset),  # material_thickness,
+    #         np.random.uniform(1, 55, n_dataset),  # material_roughness,
+
+    #         np.random.uniform(2, 6, n_dataset),  # bulk_2_sld,
+    #         np.random.uniform(20, 700, n_dataset),  # cu_thickness,
+    #         np.random.uniform(1, 55, n_dataset),  # cu_roughness,
+
     #         np.random.uniform(1, 4.2, n_dataset),  # oxide_sld,
     #         np.random.uniform(5, 50, n_dataset),  # oxide_thickness,
     #         np.random.uniform(1, 10, n_dataset),  # oxide_roughness,
     #     ]
     # )
 
+    # xx_film, 6 layer model
+    parameters_ref = np.column_stack(
+        [
+            np.random.uniform(-1.0, 7.0, n_dataset),  # electolyte_sld, including air and H2O
+            np.random.uniform(5, 120, n_dataset),  # electolyte_roughness,
+            np.random.uniform(-5.0, 6.5, n_dataset),  # sei_sld,
+            np.random.uniform(10, 500, n_dataset),  # sei_thickness,
+            np.random.uniform(1, 80, n_dataset),  # sei_roughness,
+            np.random.uniform(-2, 7, n_dataset),  # bulk_3_sld,
+            np.random.uniform(10, 200, n_dataset),  # bulk_3_thickness,
+            np.random.uniform(1, 55, n_dataset),  # bulk_3_roughness,
+            np.random.uniform(2, 7, n_dataset),  # bulk_2_sld,
+            np.random.uniform(20, 700, n_dataset),  # bulk_2_thickness (cu_thickness),
+            np.random.uniform(1, 55, n_dataset),  # bulk_2_roughness (cu_roughness),
+            np.random.uniform(-3.5, 7, n_dataset),  # bulk_1_sld,
+            np.random.uniform(10, 200, n_dataset),  # bulk_1_thickness,
+            np.random.uniform(1, 55, n_dataset),  # bulk_1_roughness,
+            np.random.uniform(1, 4.2, n_dataset),  # oxide_sld,
+            np.random.uniform(5, 50, n_dataset),  # oxide_thickness,
+            np.random.uniform(1, 10, n_dataset),  # oxide_roughness,
+        ]
+    )
+
     # generate the reference rcurves
     r_curves = np.apply_along_axis(param_to_rcurve, 1, parameters_ref)
     logger.debug(f"r_curves.shape: {r_curves.shape}")
@@ -203,12 +198,12 @@ def param_to_rcurve(param: np.ndarray) -> np.ndarray:
 
 def param_to_sld(param: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
     """Convert the parameters to the SLD profile.
-    
+
     Parameters
     ----------
     param : np.ndarray
         The parameters.
-    
+
     Returns
     -------
     Tuple[np.ndarray, np.ndarray]