diff --git a/experiments/test_1/script.py b/experiments/test_1/script.py
index 004d5dd..f6dc910 100644
--- a/experiments/test_1/script.py
+++ b/experiments/test_1/script.py
@@ -20,4 +20,13 @@
 moboqm9 = MOBOQM9(params)
 moboqm9.run_optimization()
 plt = plot_results(moboqm9.dataframe)
+# plt.savefig("figures/moboqm9_results.png")
 plt.show()
+
+"""
+panel1 => iteration vs hv [qEHVI qNEHVI random]
+panel2 => pareto front for qEHVI
+panel3 => pareto front for qNEHVI
+panel4 => pareto front for random
+"""
+
diff --git a/src/mobo_qm9.py b/src/mobo_qm9.py
index b307c70..8407a19 100644
--- a/src/mobo_qm9.py
+++ b/src/mobo_qm9.py
@@ -60,7 +60,8 @@ def __init__(self, params: MOBOQM9Parameters):
             self.params.num_total_points)
         self.features, self.targets = self.get_features_and_targets()
         self.train_indices = self.get_train_indices()
-    
+        self.dataframe = None 
+
     def get_features_and_targets(self):
         """
         Gets the features and targets for the MOBOQM9 model.
@@ -94,7 +95,7 @@ def get_surrogate_model(self):
         elif self.params.kernel == 'Matern':
             kernel = MaternKernel()
         else:
-            raise ValueError("Unsupported kernel type. Supported types are 'RBF', 'Matern', and 'Tanimoto'.")
+            raise ValueError("Unsupported kernel type. Supported types are 'RBF', and 'Matern'.")
 
         models = [SingleTaskGP(features,
                                targets[:, i].unsqueeze(-1),
diff --git a/src/utils.py b/src/utils.py
index cfc668e..990db8c 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -8,4 +8,14 @@ def plot_results(df):
     returns:
         matplotlib.pyplot.figure: Figure for the MOBOQM9 model.
     """
+    #iteration | target_1 | target_2 | target_1_qeHVI | target_2_qeHVI | target_1_qNEHVI | target_2_qNEHVI | target_1_random | target_2_random
+    # compute the global hv
+    # compute the hv for each iteration up until that point
+    # plot iteration vs hv
+    # panel 2 - 4
+    # find the pareto front of the last iteration 
+    # find the pareto front of all the data
+    # scatter plot all the data with alpha = 0.5
+    # global pareto front with blue
+    # last iteration pareto front with red *
     pass
\ No newline at end of file