diff --git a/experiments/test_1/moboqm9_results.csv b/experiments/test_1/moboqm9_results.csv
new file mode 100644
index 0000000..7eacac0
--- /dev/null
+++ b/experiments/test_1/moboqm9_results.csv
@@ -0,0 +1,953 @@
+,iteration_qEHVI,iteration_qNEHVI,iteration_random,gap,mu
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diff --git a/experiments/test_1/moboqm9_results.png b/experiments/test_1/moboqm9_results.png
new file mode 100644
index 0000000..1eb0dde
Binary files /dev/null and b/experiments/test_1/moboqm9_results.png differ
diff --git a/experiments/test_1/script.py b/experiments/test_1/script.py
index a43c8b9..e4bb158 100644
--- a/experiments/test_1/script.py
+++ b/experiments/test_1/script.py
@@ -13,22 +13,16 @@
                            surrogate_model="GaussianProcess",
                            targets=["gap", "mu"],
                            target_bools=[True, True],
-                           num_total_points=100,
-                           num_seed_points=10,
-                           n_iters=100,
-                           num_candidates=10)
+                           num_total_points=1000,
+                           num_seed_points=100,
+                           n_iters=50,
+                           num_candidates=1)
 
 moboqm9 = MOBOQM9(params)
 moboqm9.run_optimization()
 fig = plot_results(moboqm9.dataframe, [True, True])
 fig.tight_layout()
-# plt.savefig("figures/moboqm9_results.png")
+plt.savefig("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
-"""
+moboqm9.dataframe.to_csv("moboqm9_results.csv")
 
diff --git a/src/acquisition_functions.py b/src/acquisition_functions.py
index 5ac56bd..466ef1a 100644
--- a/src/acquisition_functions.py
+++ b/src/acquisition_functions.py
@@ -39,7 +39,7 @@ def optimize_qEHVI(model, reference, y_train, x_test, n_candidates):
         q=n_candidates,
         unique=True
     )
-    return torch.tensor(candidates)
+    return candidates
 
 def optimize_qNEHVI(model, reference, x_train, x_test, n_candidates):
     """
@@ -69,4 +69,4 @@ def optimize_qNEHVI(model, reference, x_train, x_test, n_candidates):
         q=n_candidates,
         unique=True
     )
-    return torch.tensor(candidates)
\ No newline at end of file
+    return candidates
\ No newline at end of file
diff --git a/src/data/cm_featurizer.py b/src/data/cm_featurizer.py
index d45d0e0..16ff4e4 100644
--- a/src/data/cm_featurizer.py
+++ b/src/data/cm_featurizer.py
@@ -1,6 +1,7 @@
 from dscribe.descriptors import CoulombMatrix
 from ase import db
-
+from pathlib import Path
+import numpy as np
 
 def get_max_number_of_atoms(indices):
     """
@@ -13,7 +14,7 @@ def get_max_number_of_atoms(indices):
         int: Maximum number of atoms for the MOBOQM9 model.
     """
     max_number_of_atoms = 0
-    with db.connect("QM9_data.db") as qm9:
+    with db.connect(str(Path(__file__).parent / "QM9_data.db")) as qm9:
         for i, row in enumerate(qm9.select()):
             if i in indices:
                 atoms = row.toatoms()
@@ -36,15 +37,23 @@ def get_coulomb_matrix(indices, targets):
     max_number_of_atoms = get_max_number_of_atoms(indices)
     cm = CoulombMatrix(n_atoms_max=max_number_of_atoms)
     
-    atoms_list, targets = [], []
-    with db.connect("QM9_data.db") as qm9:
+    atoms_list, computed_targets = [], []
+    with db.connect(str(Path(__file__).parent / "QM9_data.db")) as qm9:
         for i, row in enumerate(qm9.select()):
             if i in indices:
-                atoms_list.append(row.toatoms())
-                target_list = []
+                is_OK = True
                 for target in targets:
-                    target_list.append(row[target])
-                targets.append(target_list)
+                    try:
+                        row[target]
+                    except AttributeError:
+                        is_OK = False
+                        break
+                if is_OK:
+                    atoms_list.append(row.toatoms())
+                    target_list = []
+                    for target in targets:
+                        target_list.append(row[target])
+                    computed_targets.append(target_list)
     features = cm.create(atoms_list, n_jobs=4)
     
-    return features, targets
\ No newline at end of file
+    return features, np.array(computed_targets)
\ No newline at end of file
diff --git a/src/mobo_qm9.py b/src/mobo_qm9.py
index 7711229..7b05ad6 100644
--- a/src/mobo_qm9.py
+++ b/src/mobo_qm9.py
@@ -62,7 +62,7 @@ def __init__(self, params: MOBOQM9Parameters):
         self.dataframe = pd.DataFrame.from_dict(self.from_target_dict())
         self.acq_met = {"qEHVI": False, "qNEHVI": False, "random": False}
 
-    def form_target_dict(self):
+    def from_target_dict(self):
         """
         Forms the target dictionary for the MOBOQM9 model.
         
@@ -70,16 +70,17 @@ def form_target_dict(self):
             target_dict: Target dictionary for the MOBOQM9 model.
         """
         target_dict = {}
+        target_dict["iteration_qEHVI"] = [None] * len(self.targets)
+        target_dict["iteration_qNEHVI"] = [None] * len(self.targets)
+        target_dict["iteration_random"] = [None] * len(self.targets)
         for itarg, target in enumerate(self.params.targets):
             target_dict[target] = self.targets[:, itarg]
-            target_dict["iteration_qEHVI"] = [None] * len(self.params.targets)
-            target_dict["iteration_qNEHVI"] = [None] * len(self.params.targets)
-            target_dict["iteration_random"] = [None] * len(self.params.targets)
-            for itrain, train_mask in enumerate(self.train_indices["qEHVI"]):
-                if train_mask:
-                    target_dict["iteration_qeHVI"] = 0
-                    target_dict["iteration_qNEHVI"] = 0
-                    target_dict["iteration_random"] = 0
+            
+        for itrain, train_mask in enumerate(self.train_indices["qEHVI"]):
+            if train_mask:
+                target_dict["iteration_qEHVI"][itrain] = 0
+                target_dict["iteration_qNEHVI"][itrain] = 0
+                target_dict["iteration_random"][itrain] = 0
         return target_dict
     
     def get_features_and_targets(self):
@@ -107,9 +108,9 @@ def get_surrogate_model(self, acq):
         returns:
             model: Surrogate model for the MOBOQM9 model.
         """
-        features = torch.tensor(self.features[self.train_indices["acq"]],
+        features = torch.tensor(self.features[self.train_indices[acq]],
                                 dtype=torch.double)
-        targets = torch.tensor(self.correct_sign(self.targets[self.train_indices["acq"]]),
+        targets = torch.tensor(self.correct_sign(self.targets[self.train_indices[acq]]),
                                 dtype=torch.double)
         var = torch.full_like(targets, 1e-6)
 
@@ -122,7 +123,7 @@ def get_surrogate_model(self, acq):
 
         models = [SingleTaskGP(features,
                                targets[:, i].unsqueeze(-1),
-                               noise=var[:, i].unsqueeze(-1),
+                               var[:, i].unsqueeze(-1),
                                input_transform=Normalize(d=features.shape[-1]),
                                outcome_transform=Standardize(m=1),
                                likelihood=gpytorch.likelihoods.GaussianLikelihood(),
@@ -154,11 +155,11 @@ def optimize_acquisition_function(self, model, acq):
         returns:
             candidates: Candidates for the MOBOQM9 model.
         """
-        y_train = self.correct_sign(self.targets[self.train_indices["acq"]])
+        y_train = self.correct_sign(self.targets[self.train_indices[acq]])
         y_train = torch.tensor(y_train, dtype=torch.double)
-        x_train = torch.tensor(self.features[self.train_indices["acq"]], dtype=torch.double)
-        x_test = torch.tensor(self.features[~self.train_indices["acq"]], dtype=torch.double)
-        reference = y_train.mean(0)[0]
+        x_train = torch.tensor(self.features[self.train_indices[acq]], dtype=torch.double)
+        x_test = torch.tensor(self.features[~self.train_indices[acq]], dtype=torch.double)
+        reference = y_train.min(0)[0]
         if acq == "qEHVI":
             return optimize_qEHVI(model=model,
                                   reference=reference,
@@ -186,11 +187,11 @@ def run_optimization(self):
                 model = self.get_surrogate_model(acq)
                 if acq == "random":
                     for _ in range(self.params.num_candidates):
-                        idx = np.random.choice(np.where(~self.train_indices)[0])
+                        idx = np.random.choice(np.where(~self.train_indices[acq])[0])
                         self.train_indices[acq][idx] = True
                         self.dataframe.at[idx, "iteration_random"] = iter + 1
                 else:
-                    candidates = self.optimize_acquisition_function(model)
+                    candidates = self.optimize_acquisition_function(model, acq)
                     self.update_train_indices(candidates, acq, iter)
                 self.stopping_criteria_met(acq)
                 
@@ -204,9 +205,9 @@ def get_train_indices(self):
             train_indices: Train indices for the MOBOQM9 model.
         """
         # add latin hypercube sampling if time permits
-        temp_indices = np.random.randint(0, self.params.num_total_points,
+        temp_indices = np.random.randint(0, len(self.targets),
             self.params.num_seed_points)
-        mask = np.zeros(len(self.total_indices), dtype=bool)
+        mask = np.zeros(len(self.targets), dtype=bool)
         mask[temp_indices] = True
         return {"qEHVI": mask, "qNEHVI": mask, "random": mask}
     
diff --git a/src/utils.py b/src/utils.py
index 00ffccb..52bea09 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -1,4 +1,3 @@
-import pandas as pd
 from botorch.utils.multi_objective.box_decompositions import DominatedPartitioning
 import matplotlib.pyplot as plt
 import torch
@@ -32,9 +31,10 @@ def plot_results(df, target_bool):
     global_hv = hv.compute_hypervolume().item()
     
     hv_iterations = {"qEHVI": [], "qNEHVI": [], "random": []}
-    max_iterations = df['iteration'].max()
-    for iter_no in range(max_iterations + 1):
-        for acq in ["qEHVI", "qNEHVI", "random"]:
+    
+    for acq in ["qEHVI", "qNEHVI", "random"]:
+        max_iterations = int(df[f'iteration_{acq}'].max())
+        for iter_no in range(max_iterations + 1):
             current_data = df[df[f'iteration_{acq}'] <= iter_no]
             local_values = torch.tensor(current_data[targets].values)
             for mask in target_bool:
@@ -42,51 +42,60 @@ def plot_results(df, target_bool):
                     local_values[:, mask] *= -1
             hv = DominatedPartitioning(ref_point=ref_point, Y=local_values)
             hv_iterations[acq].append(hv.compute_hypervolume().item())
-    ax[0].plot(np.array(hv_iterations["qEHVI"])/global_hv * 100, marker='o', label="qEHVI")
-    ax[0].plot(np.array(hv_iterations["qNEHVI"])/ global_hv * 100, marker='*', label="qNEHVI")
-    ax[0].plot(np.array(hv_iterations["random"]) / global_hv, marker='s', label="random")
+    ax[0].plot(np.array(hv_iterations["qEHVI"]) / global_hv * 100, marker='o', label="qEHVI")
+    ax[0].plot(np.array(hv_iterations["qNEHVI"]) / global_hv * 100, marker='*', label="qNEHVI")
+    ax[0].plot(np.array(hv_iterations["random"]) / global_hv * 100, marker='s', label="random")
     ax[0].set_xlabel('Iteration')
     ax[0].set_ylabel('Hypervolume')
     ax[0].set_title('Hypervolume per Iteration')
     ax[0].legend(loc="center right")
 
     # Plot qEHVI
+    max_iterations = int(df['iteration_qEHVI'].max())
     current_data = df[df['iteration_qEHVI'] <= max_iterations]
     qEHVI_values = torch.tensor(current_data[targets].values)
     qEHVI_pareto_idx = is_non_dominated(qEHVI_values)
     qEHVI_pareto_front = qEHVI_values[qEHVI_pareto_idx]
-    ax[1].scatter(df[targets[0]], df[targets[1]], alpha=0.5)
-    ax[1].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1],
-        color='blue', label="Global Pareto Front", marker="*")
     ax[1].scatter(qEHVI_pareto_front[:, 0], qEHVI_pareto_front[:, 1],
-        color='red', label="qEHVI Pareto Front", marker="s")
+        color='red', label="qEHVI Pareto Front", marker="*", s=100)
+    ax[1].scatter(df[targets[0]], df[targets[1]], alpha=0.3)
+    ax[1].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1],
+        color='green', label="Global Pareto Front", marker="s")
     ax[1].set_xlabel(targets[0])
     ax[1].set_ylabel(targets[1])
+    ax[1].set_title('Pareto Front for qEHVI')
+    ax[1].legend(loc="upper right")
     
     # Plot qNEHVI
+    max_iterations = int(df['iteration_qNEHVI'].max())
     current_data = df[df['iteration_qNEHVI'] <= max_iterations]
     qNEHVI_values = torch.tensor(current_data[targets].values)
     qNEHVI_pareto_idx = is_non_dominated(qNEHVI_values)
     qNEHVI_pareto_front = qNEHVI_values[qNEHVI_pareto_idx]
-    ax[2].scatter(df[targets[0]], df[targets[1]], alpha=0.5)
-    ax[2].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1],
-        color='blue', label="Global Pareto Front", marker="*")
     ax[2].scatter(qNEHVI_pareto_front[:, 0], qNEHVI_pareto_front[:, 1],
-        color='red', label="qNEHVI Pareto Front", marker="s")
+        color='red', label="qNEHVI Pareto Front", marker="*", s=100)
+    ax[2].scatter(df[targets[0]], df[targets[1]], alpha=0.3)
+    ax[2].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1],
+        color='green', label="Global Pareto Front", marker="s")
     ax[2].set_xlabel(targets[0])
     ax[2].set_ylabel(targets[1])
+    ax[2].set_title('Pareto Front for qNEHVI')
+    ax[2].legend(loc="upper right")
     
-    # Plot qEHVI
+    # Plot random
+    max_iterations = int(df['iteration_random'].max())
     current_data = df[df['iteration_random'] <= max_iterations]
     random_values = torch.tensor(current_data[targets].values)
     random_pareto_idx = is_non_dominated(random_values)
     random_pareto_front = random_values[random_pareto_idx]
-    ax[3].scatter(df[targets[0]], df[targets[1]], alpha=0.5)
-    ax[3].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1],
-        color='blue', label="Global Pareto Front", marker="*")
     ax[3].scatter(random_pareto_front[:, 0], random_pareto_front[:, 1],
-        color='red', label="Random Pareto Front", marker="s")
+        color='red', label="Random Pareto Front", marker="*", s=100)
+    ax[3].scatter(df[targets[0]], df[targets[1]], alpha=0.3)
+    ax[3].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1],
+        color='green', label="Global Pareto Front", marker="s")
     ax[3].set_xlabel(targets[0])
     ax[3].set_ylabel(targets[1])
+    ax[3].set_title('Pareto Front for random')
+    ax[3].legend(loc="upper right")
     
     return fig
\ No newline at end of file