diff --git a/ml/Neural_Net_Classes.py b/ml/Neural_Net_Classes.py
index f59a9702..1adbcf3e 100644
--- a/ml/Neural_Net_Classes.py
+++ b/ml/Neural_Net_Classes.py
@@ -136,6 +136,7 @@ def train_model(
         exp_inputs_val,
         exp_targets_val,
         num_epochs=1500,
+        train_on_exp=1,
     ):
         for epoch in range(num_epochs):
             self.optimizer.zero_grad()
@@ -146,7 +147,8 @@ def train_model(
                 loss += nan_mse_loss(sim_targets, sim_outputs)
             if len(exp_inputs) > 0:
                 exp_outputs = self.calibrate(self(exp_inputs))
-                loss += nan_mse_loss(exp_targets, exp_outputs)
+                if train_on_exp == 1:
+                    loss += nan_mse_loss(exp_targets, exp_outputs)
             loss.backward()
 
             self.optimizer.step()
diff --git a/ml/train_model.py b/ml/train_model.py
index 065b4343..c39feb3b 100644
--- a/ml/train_model.py
+++ b/ml/train_model.py
@@ -218,6 +218,7 @@
         model = CombinedNN(len(input_names), n_outputs, learning_rate=0.0001)
         model.to(device)  # moving to GPU
         NNmodel_start_time = time.time()
+        train_on_experiments = 1
         model.train_model(
             norm_sim_inputs_train.to(device),
             norm_sim_outputs_train.to(device),
@@ -228,6 +229,7 @@
             norm_expt_inputs_val.to(device),
             norm_expt_outputs_val.to(device),
             num_epochs=20000,
+            train_on_expt
         )
         NNmodel_end_time = time.time()
         print(f"Model_{i + 1} trained in ", NNmodel_end_time - NNmodel_start_time)