diff --git a/README.md b/README.md
index 4e10fed..57f264a 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,25 @@
-[![Open in Codespaces](https://classroom.github.com/assets/launch-codespace-7f7980b617ed060a017424585567c406b6ee15c891e84e1186181d67ecf80aa0.svg)](https://classroom.github.com/open-in-codespaces?assignment_repo_id=14492361)
-# Autograding Example: Python
-This example project is written in Python, and tested with pytest.
+[![Open in Codespaces](https://classroom.github.com/assets/launch-codespace-7f7980b617ed060a017424585567c406b6ee15c891e84e1186181d67ecf80aa0.svg)](https://classroom.github.com/open-in-codespaces?assignment_repo_id=14492361)
+# Investigation of Multi-Objective Bayesian Optimization of QM9 Dataset
 
-## The assignment
-The tests are failing right now because the method isn't outputting the correct string. Fixing this up will make the tests green.
+This project will investigate the application of multi-objective Bayesian optimization (specifically EHVI- and parEGO-based methods) to benchmark several multi-objective optimization tasks with QM9 dataset. The objective is to develop specific guidelines about the choice of surrogate and acquisition functions in the context of Multi-Objective Bayesian Optimization for molecular property optimization.
 
-## Setup command
+References:
 
-See `postCreateCommand` from [`devcontainer.json`](.devcontainer/devcontainer.json).
+Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan (2020) BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
+Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp & O. Anatole von Lilienfeld (2014) Quantum chemistry structures and properties of 134 kilo molecules
 
-## Run command
-`pytest`
+# Results & Discussion
 
-## Notes
-- pip's install path is not included in the PATH var by default, so without installing via `sudo -H`, pytest would be unaccessible.
+In this work, we used the QM9 dataset (that contains about 134K molecules) to demonstrate the power of EHVI-based algorithms. In the interest of time and computational cost, we used randomly selected 1000 structures. We designed a few multi-objective tasks for this project, which are not necessarily motivated by any scientific design goals. But our results show the promise of using BO for similar kind of multi-objective molecular properties optimization. For featurization, we used the coulomb matrix (CM) and smooth overlap of atomic orbitals (SOAP) methods. For GP kernels, we used RBF, and Matern kernel. We compared three different acquisition functions, namely, 1. qEHVI, 2. qNEHVI, and 3. random data acquisition. In all the simulation results provided below, we used 50 initial random seed points. One main limitation of this work is, due to time constraints, we didn't run the simulation several times to have statistically robust results.
+
+## Maximizing mu (dielectric constant) and alpha (polarizability)
+
+This problem aims to find materials with a high dielectric constant in conjunction to a high polarizability. Below figure shows the optimization profile along with the pareto fronts for all three acquistion functions.
+
+![maximization of mu and alpha](experiments/mu_alpha/mu_alpha_results.png)
+
+# Maximizing mu and minimizing bandgap
+
+This problem is trying to find the molecules with a high dielectric constant with a low bandgap (high electrical conductivity).
+
+![Maximization of mu with minimizing gap](experiments/mu_gap/mu_gap_results.png)
diff --git a/experiments/mu_alpha/mu_alpha_results.csv b/experiments/mu_alpha/mu_alpha_results.csv
new file mode 100644
index 0000000..a7e291f
--- /dev/null
+++ b/experiments/mu_alpha/mu_alpha_results.csv
@@ -0,0 +1,955 @@
+,iteration_qEHVI,iteration_qNEHVI,iteration_random,mu,alpha
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diff --git a/experiments/mu_alpha/mu_alpha_results.png b/experiments/mu_alpha/mu_alpha_results.png
new file mode 100644
index 0000000..173e4cb
Binary files /dev/null and b/experiments/mu_alpha/mu_alpha_results.png differ
diff --git a/experiments/mu_alpha/script.py b/experiments/mu_alpha/script.py
new file mode 100644
index 0000000..01a41e1
--- /dev/null
+++ b/experiments/mu_alpha/script.py
@@ -0,0 +1,28 @@
+from pathlib import Path
+import sys
+
+import matplotlib.pyplot as plt
+
+sys.path.append(str(Path(__file__).parents[2]))
+
+from src.mobo_qm9 import MOBOQM9, MOBOQM9Parameters
+from src.utils import plot_results
+
+params = MOBOQM9Parameters(featurizer="CM",
+                           kernel="Matern",
+                           surrogate_model="GaussianProcess",
+                           targets=["mu", "alpha"],
+                           target_bools=[True, True],
+                           num_total_points=1000,
+                           num_seed_points=50,
+                           n_iters=50,
+                           num_candidates=1)
+
+moboqm9 = MOBOQM9(params)
+moboqm9.run_optimization()
+fig = plot_results(moboqm9.dataframe, [True, True])
+fig.tight_layout()
+plt.savefig("mu_alpha_results.png")
+plt.show()
+moboqm9.dataframe.to_csv("mu_alpha_results.csv")
+
diff --git a/experiments/mu_gap/mu_gap_results.csv b/experiments/mu_gap/mu_gap_results.csv
new file mode 100644
index 0000000..9a6f0eb
--- /dev/null
+++ b/experiments/mu_gap/mu_gap_results.csv
@@ -0,0 +1,967 @@
+,iteration_qEHVI,iteration_qNEHVI,iteration_random,mu,gap
+0,35.0,,,1.2401,0.2582
+1,0.0,0.0,0.0,2.827,0.2171
+2,36.0,,,4.2454,0.2488
+3,38.0,,,3.4226,0.2187
+4,39.0,,,2.2703,0.2825
+5,40.0,,,0.84,0.2326
+6,42.0,,,1.4939,0.2782
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+918,,,49.0,2.696,0.208
+919,,,15.0,3.4348,0.219
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+922,,,33.0,1.802,0.2092
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+958,,,37.0,3.4383,0.2516
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+962,,,,1.8111,0.2291
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+964,6.0,,,0.7382,0.1623
+965,,,,2.2243,0.2267
diff --git a/experiments/mu_gap/mu_gap_results.png b/experiments/mu_gap/mu_gap_results.png
new file mode 100644
index 0000000..362c285
Binary files /dev/null and b/experiments/mu_gap/mu_gap_results.png differ
diff --git a/experiments/mu_gap/script.py b/experiments/mu_gap/script.py
new file mode 100644
index 0000000..15c4ab7
--- /dev/null
+++ b/experiments/mu_gap/script.py
@@ -0,0 +1,28 @@
+from pathlib import Path
+import sys
+
+import matplotlib.pyplot as plt
+
+sys.path.append(str(Path(__file__).parents[2]))
+
+from src.mobo_qm9 import MOBOQM9, MOBOQM9Parameters
+from src.utils import plot_results
+
+params = MOBOQM9Parameters(featurizer="SOAP",
+                           kernel="Matern",
+                           surrogate_model="GaussianProcess",
+                           targets=["mu", "gap"],
+                           target_bools=[True, False],
+                           num_total_points=1000,
+                           num_seed_points=50,
+                           n_iters=50,
+                           num_candidates=1)
+
+moboqm9 = MOBOQM9(params)
+moboqm9.run_optimization()
+fig = plot_results(moboqm9.dataframe, [True, True])
+fig.tight_layout()
+plt.savefig("mu_gap_results.png")
+plt.show()
+moboqm9.dataframe.to_csv("mu_gap_results.csv")
+
diff --git a/src/data/soap_featurizer.py b/src/data/soap_featurizer.py
index ffcdc51..fb7baba 100644
--- a/src/data/soap_featurizer.py
+++ b/src/data/soap_featurizer.py
@@ -39,6 +39,10 @@ def get_soap(indices, targets):
         r_cut = 3.0,
         n_max = 4,
         l_max = 3,
+        periodic=False,
+        sparse=False,
+        average="inner",
+        rbf="gto",
     )
     
     atoms_list, computed_targets = [], []
diff --git a/src/mobo_qm9.py b/src/mobo_qm9.py
index 7b05ad6..4796f61 100644
--- a/src/mobo_qm9.py
+++ b/src/mobo_qm9.py
@@ -5,13 +5,15 @@
 from botorch.utils.multi_objective.box_decompositions import DominatedPartitioning
 from botorch.models import ModelListGP, SingleTaskGP
 import gpytorch
-from gpytorch.kernels import RBFKernel, MaternKernel
+from gpytorch.kernels import RBFKernel
 from botorch.fit import fit_gpytorch_mll
 from botorch.models.transforms.input import Normalize
 from botorch.models.transforms.outcome import Standardize
+from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
 import pandas as pd
 
 from .data.cm_featurizer import get_coulomb_matrix
+from.data.soap_featurizer import get_soap
 from .acquisition_functions import optimize_qEHVI, optimize_qNEHVI
 
 N_TOTAL_POINTS = 138_728
@@ -32,7 +34,7 @@ class MOBOQM9Parameters(NamedTuple):
         num_seed_points: Number of seed points to use. Default is 100.
         n_iters: Number of iterations. Default is 20.
     """
-    featurizer: Literal["ECFP", "CM", "ACSF"]
+    featurizer: Literal["ECFP", "CM", "SOAP"]
     kernel: Literal["RBF", "Matern"]
     surrogate_model: Literal["GaussianProcess", "RandomForest"]
     targets: List[str]
@@ -95,6 +97,9 @@ def get_features_and_targets(self):
         if self.params.featurizer == "CM":
             return get_coulomb_matrix(self.total_indices,
                 self.params.targets)
+        elif self.params.featurizer == "SOAP":
+            return get_soap(self.total_indices,
+                self.params.targets)
         else:
             raise NotImplementedError
     
@@ -117,7 +122,10 @@ def get_surrogate_model(self, acq):
         if self.params.kernel == 'RBF':
             kernel = RBFKernel()
         elif self.params.kernel == 'Matern':
-            kernel = MaternKernel()
+            kernel = gpytorch.kernels.ScaleKernel(
+                gpytorch.kernels.MaternKernel()
+                ) + gpytorch.kernels.ScaleKernel(
+                gpytorch.kernels.LinearKernel())
         else:
             raise ValueError("Unsupported kernel type. Supported types are 'RBF', and 'Matern'.")
 
@@ -132,7 +140,7 @@ def get_surrogate_model(self, acq):
                   for i in range(targets.shape[1])]
 
         model = ModelListGP(*models)
-        mll = gpytorch.mlls.SumMarginalLogLikelihood(model.likelihood, model)
+        mll = SumMarginalLogLikelihood(model.likelihood, model)
         fit_gpytorch_mll(mll)
 
         return model
@@ -256,7 +264,7 @@ def validate_params(self):
         args:
             params: Parameters for the MOBOQM9 model.
         """
-        assert self.params.featurizer in ["ECFP", "CM", "ACSF"], "Featurizer must be one of ECFP, CM, or ACSF."
+        assert self.params.featurizer in ["ECFP", "CM", "SOAP"], "Featurizer must be one of ECFP, CM, or SOAP."
         assert self.params.kernel in ["RBF", "Matern", "Tanimoto"], "Kernel must be one of RBF, Matern."
         assert self.params.surrogate_model in ["GaussianProcess", "RandomForest"], "Surrogate model must be one of GaussianProcess, or RandomForest."
         assert len(self.params.targets) == len(self.params.target_bools), "Number of targets must equal number of target booleans."
diff --git a/src/utils.py b/src/utils.py
index 52bea09..cc8cdfc 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -21,11 +21,11 @@ def plot_results(df, target_bool):
     ax = ax.flatten()
     targets = [col for col in df.columns if not col.startswith("iteration")]
     global_values = torch.tensor(df[targets].values)
-    global_pareto_idx = is_non_dominated(global_values)
-    global_pareto_front = global_values[global_pareto_idx]
     for mask in target_bool:
         if not mask:
             global_values[:, mask] *= -1
+    global_pareto_idx = is_non_dominated(global_values)
+    global_pareto_front = global_values[global_pareto_idx]
     ref_point = global_values.min(0)[0]
     hv = DominatedPartitioning(ref_point=ref_point, Y=global_values)
     global_hv = hv.compute_hypervolume().item()
@@ -54,6 +54,9 @@ def plot_results(df, target_bool):
     max_iterations = int(df['iteration_qEHVI'].max())
     current_data = df[df['iteration_qEHVI'] <= max_iterations]
     qEHVI_values = torch.tensor(current_data[targets].values)
+    for mask in target_bool:
+        if not mask:
+            qEHVI_values[:, mask] *= -1
     qEHVI_pareto_idx = is_non_dominated(qEHVI_values)
     qEHVI_pareto_front = qEHVI_values[qEHVI_pareto_idx]
     ax[1].scatter(qEHVI_pareto_front[:, 0], qEHVI_pareto_front[:, 1],
@@ -70,6 +73,9 @@ def plot_results(df, target_bool):
     max_iterations = int(df['iteration_qNEHVI'].max())
     current_data = df[df['iteration_qNEHVI'] <= max_iterations]
     qNEHVI_values = torch.tensor(current_data[targets].values)
+    for mask in target_bool:
+        if not mask:
+            qNEHVI_values[:, mask] *= -1
     qNEHVI_pareto_idx = is_non_dominated(qNEHVI_values)
     qNEHVI_pareto_front = qNEHVI_values[qNEHVI_pareto_idx]
     ax[2].scatter(qNEHVI_pareto_front[:, 0], qNEHVI_pareto_front[:, 1],
@@ -86,6 +92,9 @@ def plot_results(df, target_bool):
     max_iterations = int(df['iteration_random'].max())
     current_data = df[df['iteration_random'] <= max_iterations]
     random_values = torch.tensor(current_data[targets].values)
+    for mask in target_bool:
+        if not mask:
+            random_values[:, mask] *= -1
     random_pareto_idx = is_non_dominated(random_values)
     random_pareto_front = random_values[random_pareto_idx]
     ax[3].scatter(random_pareto_front[:, 0], random_pareto_front[:, 1],