diff --git a/baybe-inhibitor.ipynb b/baybe-inhibitor.ipynb
index a4b7f6f..c584dc7 100644
--- a/baybe-inhibitor.ipynb
+++ b/baybe-inhibitor.ipynb
@@ -10,7 +10,23 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "This project will focus on exploring the capabilities of Bayesian optimization, specifically employing BayBE, in the discovery of novel corrosion inhibitors for materials design. Initially, we will work with a randomly chosen subset from a comprehensive database of electrochemical responses of small organic molecules. Our goal is to assess how Bayesian optimization can speed up the screening process across the design space to identify promising compounds. We will compare different strategies for incorporating alloy information, while optimizing the experimental parameters with respect to the inhibitive performance of the screened compounds."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Initizalization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Loading libraries and data files:"
+   ]
   },
   {
    "cell_type": "code",
@@ -20,43 +36,131 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
+    "from baybe import Campaign\n",
     "\n",
-    "df_AA2024 = pd.read_excel('data/filtered_AA2024.xlsx')"
+    "df_AA2024 = pd.read_excel('data/filtered_AA2024.xlsx')\n",
+    "df_AA1000 = pd.read_excel('data/filtered_AA1000.xlsx')\n",
+    "df_Al = pd.read_excel('data/filtered_Al.xlsx')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "           Time_h          pH  Inhib_Concentrat_M   Efficiency\n",
-      "count  611.000000  611.000000          611.000000   611.000000\n",
-      "mean   135.801964    6.342062            0.006808    26.736841\n",
-      "std    201.683867    2.529080            0.014059   288.788317\n",
-      "min      0.500000    0.000000            0.000010 -4834.000000\n",
-      "25%     24.000000    4.000000            0.000500    30.000000\n",
-      "50%     24.000000    7.000000            0.001000    58.000000\n",
-      "75%    144.000000    7.000000            0.003000    87.950000\n",
-      "max    672.000000   10.000000            0.100000   100.000000\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "print(df_AA2024.describe())"
+    "# Data Processing"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# This is another test"
+    "# Data Anaylsis"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bayesian Optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Search Space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Objective"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Recommender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Benchmarking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transfer Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {