diff --git a/day_2/01-IntroductionDay2.ipynb b/day_2/01-IntroductionDay2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b0eb7f2eb9609571f3b539f4a348c094e99d76fe
--- /dev/null
+++ b/day_2/01-IntroductionDay2.ipynb
@@ -0,0 +1,137 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Day 2 - Parameterization of interatomic potentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will do simple fits for three different interatomic potentials.\n",
+    "* Embedded Atom Method Potential\n",
+    "* Neural Network Potential\n",
+    "* Atomic Cluster Expansion\n",
+    "\n",
+    "Some details of these potentials will be summarized in the following."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Embedded Atom Method Potential"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Atomic descriptors: pair functions\n",
+    "\n",
+    "$\\rho_i = \\sum_j \\phi(r_{ij})$ (density)\n",
+    "\n",
+    "$V_i = \\sum_j V(r_{ij})$ (pair repulsion)\n",
+    "    \n",
+    "* Atomic energy\n",
+    "\n",
+    "$E_i = F( \\rho_i ) + V_i$     \n",
+    "\n",
+    "with non-linear embedding function $F$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neural Network Potential"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Atomic descriptors: pair and three-body symmetry functions\n",
+    "\n",
+    "$G_i = \\sum_j \\phi(r_{ij})$\n",
+    "\n",
+    "$G_i = \\sum_{jk} \\phi(r_{ij},r_{ik}, \\cos_{jik})$\n",
+    "    \n",
+    "* Atomic energy\n",
+    "\n",
+    "$E_i = NN(G_i)$      \n",
+    "\n",
+    "with neural network $NN$. Various different $G_i$ are the inputs to the $NN$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Atomic Cluster Expansion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Atomic descriptors: pair, three-body, ... many-body basis functions\n",
+    "\n",
+    "$A_i = \\sum_j \\phi(\\pmb{r}_{ij})$       (many different basis functions that depend on direction and length of $r_{ij}$)\n",
+    "\n",
+    "$\\varphi_i = c_1 A_i + c_2 A_i A_i + c_3 A_i A_i A_i + ...$\n",
+    "  \n",
+    "* Atomic energy\n",
+    "\n",
+    "$E_i = F(\\varphi_i)$  \n",
+    "\n",
+    "with general non-linear function $F$ and several $\\varphi_i$. In the tutorial we will use $E_i =  \\sqrt{\\varphi^{(1)}_i} +  \\varphi^{(2)}_i$ to make contact to the Embedded Atom Method.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reference data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The potentials are parameterized by fitting to reference data. Here we use DFT data for Cu that we generated with the FHI-aims code. In the following we summarize key properties of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}