diff --git a/exploratory_analysis.ipynb b/exploratory_analysis.ipynb
index 983533ea8e8b5168a15d002ccd766243552980c9..f0ac253b4adadcadedaee1c86c108bc1eb8e927a 100644
--- a/exploratory_analysis.ipynb
+++ b/exploratory_analysis.ipynb
@@ -36,7 +36,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial, we use unsupervised learning for a preliminary exploration of materials science data. More specifically, we analyze 82 octet binary materials known to crystallize in zinc blende (ZB) and rocksalt (RS) structures. Our aim is to show how to facilitate the visualization of unlabeled data and gain an understanding of the relevant inner structures inside the dataset. As a first step in our data analysis, we would like to detect whether data points can be classified into different  clusters, where each cluster is aimed to group together objects that share similar features. With an explorative analysis we would like to visualize the structure and spatial displacement of the clusters, but when the feature space is higlhly multidimensional such visualization is directly not possible. Hence, we project the feature space onto a two-dimensional manifold which, instead, can be  visualized. To avoid losing relevant information, the embedding into a lower dimensional manifold must be performed while preserving the most informative features in the original space. Below we introduce into different clustering and embedding methods, which can be combined to obtain different visualizations of our dataset."
+    "In this tutorial, we use unsupervised learning for an exploratory analysis of materials science data. More specifically, we analyze 82 octet binary materials known to crystallize in zinc blende (ZB) and rocksalt (RS) structures. Our aim is to show how to visualize a multidimensional dataset and gain an understanding of its relevant inner structures. As a first step in our data analysis, we would like to detect whether data points can be classified into different clusters, where each cluster is aimed to group together objects that share similar features. With an explorative analysis we would like to visualize the structure and spatial arrangement of the clusters, but when the feature space is highly multidimensional such visualization is directly not possible. Hence, we project the feature space onto a two-dimensional manifold which, instead, can be visualized. To avoid losing relevant information, embedding into a lower dimensional manifold must be performed while preserving the most informative features in the original space. Below we introduce into different clustering and embedding methods, which can be combined to obtain different visualizations of our dataset."
    ]
   },
   {
@@ -50,12 +50,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Cluster analysis is performed to group together data points that are more similar to each other in comparison with points belonging in other clusters. Clustering can be achieved by means of many different algorithms, each with proper characteristics and input parameters. The choice of the clustering algorithms to be used depends on the specific dataset analyzed, and, once an optimal algorithm has been chosen, it is often necessary to iteratively modify the input parameters until results achieve the desired resolution. We focus on four different algorithms as described below.\n",
-    "- ___k_-means__ partitions the data set into _k_ clusters, where each datapoint belongs in the cluster with the nearest mean. This partition ultimately minimizes the within-cluster variance to find the most compact partitioning of the data set. _k_-means uses an iterative refinement technique that is fast and scalable, but if falls in local minima. Thus, the algorithm is iterated multiple times with different initial conditions and the best outcome is finally chosen. Drawbacks of this algorithm are that the number of clusters _k_ is an input parameter which must be known in advance and clusters are convex shaped.\n",
-    "- __Hierarchical clustering__ builds a hierarchy of clusters with a bottom-up (__agglomerative__) or top-down (__divisive__) approach. In a bottom-up approach, that we deploy in this tutorial, starting with all datapoints placed in its own cluster, different pairs of clusters are iteratively merged together where the decision of the clusters to be merged is determined in a greedy manner. This is iterated until all points are grouped within one cluster, and the resulting hierarchy of clusters is presentend in a dendrogram. If a distance threshold is given, clusters are not merged if they are more distant than the threshold value, and this stops the algorithm when no more mergings are possible. The algorithm then returns a certain number of clusters as a function of the threshold distance. An advantage of this algorithm is that the construction of dendroids allows for a visual inspection of the clustering, but hierarchical clustering is a rather slow algorithm and not well suited for big data.\n",
-    "- Density-based spatial clustering of applications with noise (__DBSCAN__) is an algorithm that, without knowing the exact number of clusters, groups points that are close to each other leaving outliers marked as noise and not defined in any clusters. In this algorithm,  a neighborood distance _$\\epsilon$_  and a number of points _min-samples_ are used to determine whether a point belongs in a cluster: in case the point has a number _min-samples_ of other points  within the distance _$\\epsilon$_ is marked as core point and belongs to a cluster; otherwise, the point is marked as noise. This algorithm is fast and clusters can assume any shapes, but the choice of the distance _$\\epsilon$_ migth be non trivial.\n",
-    "-__HDBSCAN__ is a hierarchical extension of DBSCAN. This algorithm deploys the mutual reachability distance as distance metric to push outliers away from high density regions, thus facilitating noise detection. The mutual reachability distance acts by increasing the distance of all points that are not close to at least _min_samples_ points. Using this metric, the algorithm builds a hierarchy tree, where it extracts clusters which contain at least _min_cluster_size_ elements.\n",
-    "- The fast search and find of density peaks (__DenPeak__) algorithm is a density-based algorithm that is able to automatically locate non-spherical clusters. Density peaks are assumed to be sourrounded by lower density regions. Based on the position of the highest density peak, the peaks can be visualized on a graph that shows their sourrounding density and the distance from the first peak. It is then possible to choose the peaks to include from this plot, where each peak represents a different cluster."
+    "Cluster analysis is performed to group together data points that are more similar to each other in comparison with points belonging in other clusters. Clustering can be achieved by means of many different algorithms, each with proper characteristics and input parameters. The choice of the clustering algorithms to be used depends on the specific dataset analyzed, and once an optimal algorithm has been chosen it is often necessary to iteratively modify the input parameters until results achieve the desired resolution. We focus on five different algorithms as described below.\n",
+    "- ___k_-means__ partitions the dataset into _k_ clusters, where each datapoint belongs in the cluster with the nearest mean. This partition ultimately minimizes the within-cluster variance to find the most compact partitioning of the data set. _k_-means uses an iterative refinement technique that is fast and scalable, but if falls in local minima. Thus, the algorithm is iterated multiple times with different initial conditions, and the best outcome is finally chosen. Drawbacks of this algorithm are that the number of clusters _k_ is an input parameter which must be known in advance and clusters are convex shaped.\n",
+    "- __Hierarchical clustering__ builds a hierarchy of clusters with a bottom-up (__agglomerative__) or top-down (__divisive__) approach. In this tutorial we deploy a bottom-up approach. In a bottom-up hierarchical clustering algorithm, all datapoints are initially placed into its own cluster, thus the number of clusters is initially equal to the number of datapoints. Then different pairs of clusters are iteratively merged together where the decision of the clusters to be merged is made according to a specific linkage criterion. Merging is iterated until all points are grouped into a unique supercluster, and the resulting hierarchy of clusters can be shown with means of a dendrogram. If a distance threshold is given, clusters are not merged if they are more distant than the threshold value, and this stops the algorithm when no more mergings are possible. The algorithm then returns a certain number of clusters as a function of the threshold distance. An advantage of this algorithm is that the construction of dendroids allows for a visual inspection of the clustering, but hierarchical clustering is a rather slow algorithm and not well suited for big data.\n",
+    "- Density-based spatial clustering of applications with noise (__DBSCAN__) is an algorithm that, without knowing the exact number of clusters, groups points that are close to each other leaving outliers marked as noise and not defined in any clusters. In this algorithm, a neighborood distance _$\\epsilon$_ and a number of points _min-samples_ are used to determine whether a point belongs in a cluster: in case the point has a number _min-samples_ of other points within the distance _$\\epsilon$_ is marked as core point and belongs in a cluster; otherwise, the point is marked as noise. This algorithm is fast and clusters can assume any shapes, but the choice of the distance _$\\epsilon$_ migth be non trivial.\n",
+    "- __HDBSCAN__ is a hierarchical extension of DBSCAN. This algorithm deploys the mutual reachability distance as distance metric to push outliers away from high density regions, thus facilitating their detection. The mutual reachability distance acts by increasing the distance of all points that are not close to at least _min_samples_ points. Using this metric, the algorithm builds a hierarchy tree, where it extracts clusters which contain at least _min_cluster_size_ elements.\n",
+    "- The fast search and find of density peaks (__DenPeak__) algorithm is a density-based algorithm that makes use of a bidimensional plot to select which clusters are extracted. Density peaks are assumed to be surrounded by lower density regions. Based on the position of the highest density peak, the peaks can be visualized on a graph that shows their surrounding density and the distance from the most densely surrounded peak. It is then possible to choose the peaks to include from this plot, where each peak is a point in the plot."
    ]
   },
   {
@@ -69,10 +69,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Visualization of a dataset is not possible when it is defined in a highly multidimensional space, but a visual analysis can help to detect inner structures in the dataset. Hence, in order to make such visualization possible, we reduce the dimensionality of the system with methodologies specifically developed to avoid losing critical information during the embedding into a lower dimensionality space. In this tutorial, we use three different embedding methods that are summarized below.\n",
+    "Visualization of a dataset is not possible when it is defined in a highly multidimensional space, but a visual analysis can help detecting inner structures in the dataset. Hence, in order to make such visualization possible, we reduce the dimensionality of the system using an embedding algorithm.\n",
+    "These methods are specifically developed to avoid losing critical information during  embedding into a lower dimensionality space. In this tutorial, we use three different embedding algorithms that are summarized below.\n",
     "- Principal component analysis (__PCA__) is a linear projection method that seeks for an orthogonal transformation of the dataset so as to render the variables of the dataset uncorrelated. The dimensionality reduction is then performed onto the features with highest variance to preserve as much information as possible. This is a deterministic but linear method, that fails to catch non linear correlations.\n",
-    "- Multi-dimensional scaling (__MDS__) constructs a pairwise distance matrix in the original space, and seeks a low-dimensional representation that preserves the original distances as much as possible. This method tends to preserve local structures better than global structures and scales badly with the number of data points. \n",
-    "- T-distributed Stochastic Neighbor Embedding (__t-SNE__) is a non-linear dimensionality reduction method that converts similarities between data points to joint probabilities and minimizes the Kullback-Leibler divergence between the joint probabilities of the embedding and the original space. The cost function is not convex and results depend on the inizialization. Non linear effects in this method might occasionally produce misleading results, therefore several iterations of the method are recommended.\n"
+    "- Multidimensional scaling (__MDS__) constructs a pairwise distance matrix in the original space and seeks a low-dimensional representation that preserves the original distances as much as possible. This method tends to preserve local structures better than global structures and scales badly with the number of data points. \n",
+    "- T-distributed Stochastic Neighbor Embedding (__t-SNE__) is a non-linear dimensionality reduction method that converts similarities between data points to joint probabilities and minimizes the Kullback-Leibler divergence between the joint probabilities of the embedding and the original space. The cost function is not convex, and results depend on the initialization. Nonlinear effects in this method might occasionally produce misleading results, therefore several iterations of the method are recommended."
    ]
   },
   {
@@ -86,16 +87,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We load below the packages required for the tutorial. Most of the clustering and embedding algorithms are contained in the scikit-learn package. We use panda's dataframe to manipulate our dataset."
+    "We load below the packages required for the tutorial. Most of the clustering and embedding algorithms are contained in the scikit-learn and SciPy packages. We use Panda's dataframe for manipulating our dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 303,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T18:58:58.492480Z",
-     "start_time": "2021-01-03T18:58:58.487049Z"
+     "end_time": "2021-01-04T16:28:07.755442Z",
+     "start_time": "2021-01-04T16:28:07.095624Z"
     }
    },
    "outputs": [],
@@ -104,15 +105,14 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "from scipy.cluster.hierarchy import dendrogram, linkage, cut_tree\n",
-    "from scipy.cluster.hierarchy import dendrogram\n",
-    "from sklearn import preprocessing, svm\n",
+    "from sklearn import preprocessing\n",
     "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
     "from sklearn.decomposition import PCA\n",
     "from sklearn.manifold import TSNE, MDS\n",
-    "from sklearn.svm import SVC\n",
-    "from sklearn.model_selection import train_test_split\n",
+    "# from sklearn.svm import SVC\n",
+    "# from sklearn.model_selection import train_test_split\n",
     "import hdbscan\n",
-    "import plotly.express as px\n",
+    "# import plotly.express as px\n",
     "import plotly.graph_objects as go\n",
     "import ipywidgets as widgets\n",
     "from IPython.display import display, clear_output\n",
@@ -122,11 +122,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T17:13:20.488851Z",
-     "start_time": "2021-01-03T17:13:20.485901Z"
+     "end_time": "2021-01-04T16:28:07.758936Z",
+     "start_time": "2021-01-04T16:28:07.756961Z"
     }
    },
    "outputs": [],
@@ -154,11 +154,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T17:13:37.256376Z",
-     "start_time": "2021-01-03T17:13:37.174087Z"
+     "end_time": "2021-01-04T16:28:07.870608Z",
+     "start_time": "2021-01-04T16:28:07.760577Z"
     },
     "scrolled": true
    },
@@ -209,16 +209,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We insert in the dataframe a column that contains different marker symbols for different most stable structure types. These markers will be used while visualizing the datapoints on the 2-dimensional embedding."
+    "We insert in the dataframe a column that contains different marker symbols for different most stable structure types. These markers will be used while visualizing the datapoints in the 2-dimensional embedding."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 264,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T17:45:05.445766Z",
-     "start_time": "2021-01-03T17:45:05.439960Z"
+     "end_time": "2021-01-04T16:28:07.876974Z",
+     "start_time": "2021-01-04T16:28:07.872057Z"
     }
    },
    "outputs": [],
@@ -230,16 +230,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A 'Clustering' class is defined that includes all clustering algorithms that are covered during the tutorial. Before creating an instance of this class, a dataframe variable 'df' must have been defined. In this class, the clustering functions gives labels to the entries in the dataframe according to the outcome of the clustering assignments."
+    "A 'Clustering' class is defined that includes all clustering algorithms that are covered during the tutorial. Before creating an instance of this class, a dataframe variable 'df' must have been defined. In this class, each clustering function labels the entries in the dataframe according to the outcome of the cluster assignment."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 370,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:23:14.144040Z",
-     "start_time": "2021-01-03T19:23:14.132942Z"
+     "end_time": "2021-01-04T16:28:07.896229Z",
+     "start_time": "2021-01-04T16:28:07.878762Z"
     }
    },
    "outputs": [],
@@ -303,16 +303,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The embedding algorithms are handled with a graphical interface that is generated using Jupyter Widgets, that allows to generate a plot with the desired embedding algorithm by pushing a button. Before plotting data with any of the embedding algorithms, a dataframe 'df' must have been defined, and cluster labels assigned to each data point."
+    "The embedding algorithms are handled with a graphical interface that is generated using Jupyter Widgets, that allows to create plots using the desired embedding algorithm. Before plotting data with any of the embedding algorithms, a dataframe 'df' must have been defined, and cluster labels must have been assigned to each data point."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 280,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T18:03:38.688564Z",
-     "start_time": "2021-01-03T18:03:38.669764Z"
+     "end_time": "2021-01-04T16:28:07.911638Z",
+     "start_time": "2021-01-04T16:28:07.898218Z"
     }
    },
    "outputs": [],
@@ -328,11 +328,6 @@
     "        method = str (obj.description)\n",
     "\n",
     "        try:\n",
-    "            df \n",
-    "        except NameError:\n",
-    "            print(\"Please define a dataframe 'df'\")\n",
-    "            return\n",
-    "        try:\n",
     "            df['clustering'][0]\n",
     "        except KeyError:\n",
     "            print(\"Please assign labels with a clustering algorithm\")\n",
@@ -367,7 +362,7 @@
     "                                                ))[0]\n",
     "                scatter['hovertemplate']=r\"<b>%{text}</b><br><br> Low energy structure:  %{customdata[0]}<br>Cluster label:  %{customdata[1]}<br>\"\n",
     "                scatter['marker'].symbol=df[df['cluster_label']==cl]['marker_symbol'].to_numpy()\n",
-    "                scatter['text']=df.index.to_list()\n",
+    "                scatter['text']=df[df['cluster_label']==cl].index.to_list()\n",
     "                            \n",
     "            fig.update_layout(\n",
     "                plot_bgcolor='rgba(229,236,246, 0.5)',\n",
@@ -409,16 +404,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We select which features will be used for the clustering and embedding methods. The complexity of the problem clearly decreases as the number of features is reduced, and an accurate selection of the features to be processed can improve the quality of the results. To find the most meaningful results, it is sometimes necessary to iterate training while considering different features at each iteration.  "
+    "We select which features will be used for the clustering and embedding algorithms. The complexity of the problem clearly decreases as the number of features is reduced, and an accurate selection of the features to be processed can improve the quality of the results. To find the most meaningful results, it is sometimes necessary to iterate training while considering different features at each iteration.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 281,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T18:03:39.335323Z",
-     "start_time": "2021-01-03T18:03:39.331734Z"
+     "end_time": "2021-01-04T16:28:07.926241Z",
+     "start_time": "2021-01-04T16:28:07.913288Z"
     }
    },
    "outputs": [],
@@ -446,16 +441,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Feature standardization is the operation of rescaling data so as to be shaped as a Gaussian with zero mean and unit variance, and it is a common requirement for machine learning algorithms. In fact, estimators can be biased towards dimensions presenting higher absolute values, or outliers can undermine the learning capabilites  of the algorithm. Hence, we standardize our dataset by subtracting the mean value and dividing it by the standard deviation for each variable."
+    "Feature standardization is the operation of rescaling data so as to be shaped as a Gaussian with zero mean and unit variance, and it is a common requirement for machine learning algorithms. In fact, estimators can be biased towards dimensions presenting higher absolute values, or outliers can undermine the learning capabilites  of the algorithm. Hence, we standardize the dataset by subtracting the mean value and dividing it by the standard deviation for each variable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 282,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T18:03:39.759950Z",
-     "start_time": "2021-01-03T18:03:39.749501Z"
+     "end_time": "2021-01-04T16:28:07.946990Z",
+     "start_time": "2021-01-04T16:28:07.928697Z"
     },
     "scrolled": true
    },
@@ -468,33 +463,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Panda's dataframes offer a number of useful tools to visualize datasets. For example, here we show histograms of all 'features' for all entries in the dataframe by calling the 'hist' function. Below we see that the dataset has been normalized."
+    "Panda's dataframes offer a number of useful tools to visualize datasets. For example, here we show histograms of all 'features' for all entries in the dataframe by calling the 'hist' function. Below we notice that the dataset has been normalized."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 283,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T18:03:41.897594Z",
-     "start_time": "2021-01-03T18:03:40.216149Z"
+     "end_time": "2021-01-04T16:28:09.611115Z",
+     "start_time": "2021-01-04T16:28:07.948604Z"
     },
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1080 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "hist = df[features].hist( bins=10, figsize = (20,15));"
    ]
@@ -518,16 +500,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From the class clustering, we call the 'kmeans' function with the desired values of clusters and maximum iterations as parameters. The function will then assign to the materials stored in the dataframe 'df' the label of the cluster they belong in."
+    "From the class 'clustering', we call the 'kmeans' function with the desired values of clusters and maximum iterations as parameters. The function will then assign to the materials in the dataframe 'df' the label of the cluster they belong in."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 313,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:02:53.287520Z",
-     "start_time": "2021-01-03T19:02:53.255273Z"
+     "end_time": "2021-01-04T16:28:09.633401Z",
+     "start_time": "2021-01-04T16:28:09.612488Z"
     },
     "scrolled": true
    },
@@ -540,32 +522,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 314,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:02:53.413468Z",
-     "start_time": "2021-01-03T19:02:53.406593Z"
+     "end_time": "2021-01-04T16:28:09.638358Z",
+     "start_time": "2021-01-04T16:28:09.634718Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AgBr    1\n",
-      "AgCl    1\n",
-      "AgF     1\n",
-      "AgI     0\n",
-      "AlAs    0\n",
-      "AlN     1\n",
-      "AlP     0\n",
-      "AlSb    0\n",
-      "AsGa    0\n",
-      "AsB     0\n",
-      "Name: cluster_label, dtype: int32\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(df['cluster_label'][:10])"
    ]
@@ -578,35 +542,20 @@
     "\n",
     "Now we deploy the graphical interface defined above to visualize the datapoints using a two dimensional embedding of our choice.\n",
     "The function 'show_embedding' displays three buttons labeled with the name of the dimension reduction methods that are deployed in this tutorial.\n",
-    "Clicking any of the buttons will show a plot of the dataset that uses the relative two dimensional embedding. "
+    "Clicking any of the buttons will show a plot of the dataset that uses the relative embedding. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 315,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:02:53.762957Z",
-     "start_time": "2021-01-03T19:02:53.685207Z"
+     "end_time": "2021-01-04T16:28:09.891273Z",
+     "start_time": "2021-01-04T16:28:09.639781Z"
     },
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b264423d80e64d9c8b11bfbf0d31ae36",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(Button(description='PCA', style=ButtonStyle()), Button(description='MDS', style=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_embedding()"
    ]
@@ -615,7 +564,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the plot, different clusters are visualized with different colors, and by hovering over points it is possible to see the name of the relative material, its most stable structure and the cluster number it was assigned to.\n",
+    "In the plot, different clusters are visualized with different colors, and by hovering over points it is possible to see the name of the relative material, its most stable structure and the cluster it was assigned to.\n",
     "\n",
     "We can see open squares and hexagrams used as markers in the plot. Open squares indicate materials whose most stable structure is rocksalt, while hexograms are used for zinc blende structures. Can you modify the code so as to visualize rocksalt as diamond and zinc blende as open circle? A more difficult task is to modify the hovering features. Can you add the atomic number of the two elements to the hovering features? A hint is that text visualized in the cell appearing while hovering  is defined as 'hovertemplate' in the 'show_embedding' function. Then few other modifications are required. Now inspect the atomic number values for the different materials. Are such values as you would expect, i.e. natural numbers? If not, can you explain why they are not?\n",
     "\n",
@@ -629,11 +578,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 293,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T18:50:06.115626Z",
-     "start_time": "2021-01-03T18:50:06.106963Z"
+     "end_time": "2021-01-04T16:28:09.898070Z",
+     "start_time": "2021-01-04T16:28:09.892737Z"
     }
    },
    "outputs": [],
@@ -656,69 +605,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 302,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T18:53:25.093724Z",
-     "start_time": "2021-01-03T18:53:25.061959Z"
+     "end_time": "2021-01-04T16:28:09.932800Z",
+     "start_time": "2021-01-04T16:28:09.899348Z"
     },
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "      <th>RS</th>\n",
-       "      <th>ZB</th>\n",
-       "      <th>Materials in cluster</th>\n",
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-       "  <tbody>\n",
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-      ],
-      "text/plain": [
-       "   RS  ZB Materials in cluster\n",
-       "0  34  65                   52\n",
-       "1  76  23                   30"
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "composition_RS_ZB(df)"
    ]
@@ -729,7 +624,7 @@
    "source": [
     "We can see that $k$-means finds two distinct clusters, and in one of the two clusters there are more 'RS' stable structures while in the other there are more 'ZB' stable structures. This is a hint that in the space described by the atomic features, materials with the same most stable structure are close to each other, that is also possible to visualize using the different embedding algorithms. \n",
     "\n",
-    "Observing the linear and deterministic embedding given by PCA we can clearly notice that RS and ZB structures are placed in different regions of the embedding space. But we notice that there is an overlapping area where RS and ZB are close to each other, where assessing the most stable structure is difficult if assessment is solely based on the specific location in the PCA embedding. We can also notice that the volume spanned by RS structures seems to be larger with respect to ZB structrues. On the other hand, we know that $k$-means is only able to detect convex clusters of comparable shapes, hence we can argue that it might not be able to find the desired two clusters."
+    "Observing the linear and deterministic embedding given by PCA we can clearly notice that RS and ZB structures are placed in different regions of the embedding space. But we notice that there is an overlapping area where RS and ZB are close to each other. We can also notice that the volume spanned by RS structures seems to be larger with respect to ZB structrues. On the other hand, we know that $k$-means is only able to detect convex clusters of comparable shapes, hence we can argue that it might not be able to find the desired two clusters."
    ]
   },
   {
@@ -750,11 +645,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 385,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:43:07.632638Z",
-     "start_time": "2021-01-03T19:43:07.613870Z"
+     "end_time": "2021-01-04T16:28:09.945953Z",
+     "start_time": "2021-01-04T16:28:09.934125Z"
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     "scrolled": true
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@@ -767,99 +662,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 386,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:43:07.838054Z",
-     "start_time": "2021-01-03T19:43:07.763636Z"
+     "end_time": "2021-01-04T16:28:10.020006Z",
+     "start_time": "2021-01-04T16:28:09.947670Z"
     },
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7495e8896a1d4f56b8050bbd329288da",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(Button(description='PCA', style=ButtonStyle()), Button(description='MDS', style=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_embedding()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 387,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:43:08.416812Z",
-     "start_time": "2021-01-03T19:43:08.386876Z"
+     "end_time": "2021-01-04T16:28:10.049290Z",
+     "start_time": "2021-01-04T16:28:10.021782Z"
     },
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "   RS  ZB Materials in cluster\n",
-       "0  86  13                   44\n",
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-      ]
-     },
-     "execution_count": 387,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "composition_RS_ZB(df)"
    ]
@@ -875,27 +701,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 365,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:19:11.636111Z",
-     "start_time": "2021-01-03T19:19:11.503934Z"
+     "end_time": "2021-01-04T16:28:10.213386Z",
+     "start_time": "2021-01-04T16:28:10.051024Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Z = linkage(df[features], 'ward' )\n",
     "dendrogram(Z, truncate_mode='lastp',p=11);"
@@ -920,18 +733,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "DBSCAN is a density-based spatial clustering algorithm that detects noise and is able to extract clusters of different size and shape.\n",
-    "This algorithm requires two parameters: the distance $\\epsilon$ is the maximum distance for considering two points neighbours; _min_samples_ gives the minimum number of neighbor required to define a core point. \n",
+    "DBSCAN is a density-based clustering algorithm that detects noise and is able to extract clusters of different size and shape.\n",
+    "This algorithm requires two parameters: the distance $\\epsilon$ is the maximum distance for considering two points as neighbours; _min_samples_ gives the minimum number of neighbors required to define a core point. \n",
     "Core points are the core component of clusters, and all those points that are neither core points nor neighbor of core points are labeled as noise.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 416,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:48:28.447927Z",
-     "start_time": "2021-01-03T19:48:28.432013Z"
+     "end_time": "2021-01-04T16:28:10.219907Z",
+     "start_time": "2021-01-04T16:28:10.214805Z"
     }
    },
    "outputs": [],
@@ -943,98 +756,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 417,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:48:28.641161Z",
-     "start_time": "2021-01-03T19:48:28.572840Z"
+     "end_time": "2021-01-04T16:28:10.300990Z",
+     "start_time": "2021-01-04T16:28:10.221245Z"
     },
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "819d75a896b14216a17b3d3588f7663d",
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(Button(description='PCA', style=ButtonStyle()), Button(description='MDS', style=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_embedding()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 418,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T19:48:30.831414Z",
-     "start_time": "2021-01-03T19:48:30.798968Z"
+     "end_time": "2021-01-04T16:28:10.328403Z",
+     "start_time": "2021-01-04T16:28:10.302447Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>RS</th>\n",
-       "      <th>ZB</th>\n",
-       "      <th>Materials in cluster</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>70</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   RS   ZB Materials in cluster\n",
-       "0   0  100                   17\n",
-       "1  70   30                   30"
-      ]
-     },
-     "execution_count": 418,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "composition_RS_ZB(df)"
    ]
@@ -1044,9 +788,9 @@
    "metadata": {},
    "source": [
     "We can see that the algorithm has found two different clusters, and we notice that each cluster is representative of the RS vs ZB structure. However, this happens at the cost of neglecting many points that have been classified as noise.\n",
-    "Now tune the parameters and see the effect of each parameter on the amount of noise.\n",
+    "Now tune the parameters and see the effects of each parameter on the amount of noise.\n",
     "\n",
-    "Considering that MDS seeks for an embedding that tries to preserve local pairwise distances, we would expect that in a MDS embedding noise is placed far from the defined clusters. Differently t-SNE tends to privilege global structures at the expenses of losing local definition, hence noise can be placed closed to other clusters. In fact, it is possible to notice that using t-SNE points tend to be equally distanced from each other, but clusters are quite distinguishable. Pairwise distances are not meaningful in a t-SNE embedding because it aims to depict global arrangements of clusters. On the other hand, MDS sometimes fails to arrange the different clusters.\n",
+    "Considering that MDS seeks for an embedding that tries to preserve local pairwise distances, we would expect that in a MDS embedding noise is placed far from the defined clusters. Differently t-SNE tends to privilege global structures at the expenses of losing local definition, hence noise can be placed closed to other clusters. In fact, it is possible to notice that using t-SNE points tend to be equally distanced from each other, but clusters are quite distinguishable. Pairwise distances are not meaningful in a t-SNE embedding because it aims to depict global arrangements of clusters. On the other hand, MDS attemmpting to preserve all pairwise distances sometimes fails to arrange the different clusters.\n",
     "\n",
     "Can you notice in this case that noise is better isolated in a MDS embedding rather than using a t-SNE embedding? Try using a small amount of noise by tuning down the parameters for an easier visualization."
    ]
@@ -1066,17 +810,17 @@
     "HDBSCAN can be defined as a hierarchical extension of DBSCAN, with respect to which it has a number of advantages. \n",
     "One advantage is that there is only one relevant parameter to be tuned, i.e. the minimum size of clusters. \n",
     "This parameter is more intuitive to assess in comparison to e.g. the $\\epsilon$ threshold in DBSCAN.\n",
-    "In the HDBSCAN library we deploy, the minimum number of samples that is used for the mutual reachability distance is  by default fixed to the same value of the minimum cluster size, as they both have the same objective. \n",
+    "In the HDBSCAN library that we we deploy, the minimum number of samples that is used for the mutual reachability distance is  by default fixed to the same value of the minimum cluster size, as they essentiallt have the same goal, i.e. avoid the detection of clusters that contain less than a certain number of objects. \n",
     "In this tutorial we explicitly define the two values. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 425,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T20:23:27.932727Z",
-     "start_time": "2021-01-03T20:23:27.916036Z"
+     "end_time": "2021-01-04T16:28:10.349230Z",
+     "start_time": "2021-01-04T16:28:10.329658Z"
     }
    },
    "outputs": [],
@@ -1088,97 +832,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 426,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T20:23:28.187432Z",
-     "start_time": "2021-01-03T20:23:28.113866Z"
+     "end_time": "2021-01-04T16:28:10.427573Z",
+     "start_time": "2021-01-04T16:28:10.351211Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f1c6cdf4e83a4a15b17adcd5b2783dc3",
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(Button(description='PCA', style=ButtonStyle()), Button(description='MDS', style=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_embedding()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 427,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T20:23:28.334834Z",
-     "start_time": "2021-01-03T20:23:28.313888Z"
+     "end_time": "2021-01-04T16:28:10.447426Z",
+     "start_time": "2021-01-04T16:28:10.429033Z"
     }
    },
-   "outputs": [
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>82</td>\n",
-       "      <td>17</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   RS   ZB Materials in cluster\n",
-       "0   0  100                   12\n",
-       "1  82   17                   17"
-      ]
-     },
-     "execution_count": 427,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "composition_RS_ZB(df)"
    ]
@@ -1209,27 +884,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 295,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T18:50:38.962692Z",
-     "start_time": "2021-01-03T18:50:38.839100Z"
+     "end_time": "2021-01-04T16:28:10.588857Z",
+     "start_time": "2021-01-04T16:28:10.448746Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Clustering().dpc()"
    ]
@@ -1239,140 +901,51 @@
    "metadata": {},
    "source": [
     "In the plot above, each point represents a different peak that could be the core of a specific cluster if selected.\n",
-    "All points of the dataset are placed in the plot, and in the top right positon of the plot we always have one point that represents the highest density point. \n",
+    "All points of the dataset are placed in the plot, and in the top right position of the plot we always have one point that represents the peak in the highest density region. \n",
     "The other peaks are then placed in the plot according to their local density and distance ('delta' in the graph) from the first peak. \n",
-    "We choose the values on the x,y-axis, and the algorithm will return the clusters that are given by the peaks that are selected. \n",
+    "Choosing the values on the x,y-axis, it is possible to select the clusters that the algorithm returns.\n",
     "Here, we select the 3 peaks closest to the vertex."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 430,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T20:40:20.761084Z",
-     "start_time": "2021-01-03T20:40:20.634588Z"
+     "end_time": "2021-01-04T16:28:10.705792Z",
+     "start_time": "2021-01-04T16:28:10.590212Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Clustering().dpc(2.4,3.8)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 431,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T20:40:29.271435Z",
-     "start_time": "2021-01-03T20:40:29.186173Z"
+     "end_time": "2021-01-04T16:28:10.772478Z",
+     "start_time": "2021-01-04T16:28:10.707090Z"
     },
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7539aab6e8914dd399f92e6478218b64",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(Button(description='PCA', style=ButtonStyle()), Button(description='MDS', style=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_embedding()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 432,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-03T20:40:54.079754Z",
-     "start_time": "2021-01-03T20:40:54.041534Z"
+     "end_time": "2021-01-04T16:28:10.803036Z",
+     "start_time": "2021-01-04T16:28:10.773905Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>RS</th>\n",
-       "      <th>ZB</th>\n",
-       "      <th>Materials in cluster</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>100</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>100</td>\n",
-       "      <td>0</td>\n",
-       "      <td>32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>37</td>\n",
-       "      <td>62</td>\n",
-       "      <td>24</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    RS   ZB Materials in cluster\n",
-       "0    0  100                   26\n",
-       "1  100    0                   32\n",
-       "2   37   62                   24"
-      ]
-     },
-     "execution_count": 432,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "composition_RS_ZB(df)"
    ]
@@ -1382,10 +955,10 @@
    "metadata": {},
    "source": [
     "We have found two clusters containing only materials with the same most stable structure and a mixed cluster containing both most stable structures. \n",
-    "It is interesting to visualzie this result with MDS, where we can see that the mixed cluster is placed in between the pure clusters as a transition zone.\n",
+    "It is interesting to visualize this result with MDS, where we can see that the mixed cluster is placed in between the pure clusters as a transition zone.\n",
     "\n",
-    "Results of this clustering suggest that the features we have used are sufficient for classifying materials according to their most stable structure.\n",
-    "Even though the RS and ZB clusters are not clearly separated as a mixed cluster transitioning between the two clusters is found, a supervised machine learning model might be able to learn classification of the 82 octet binary materials.\n",
+    "Results of this clustering suggest that the atominc features we have used are sufficient for classifying materials according to their most stable structure.\n",
+    "Even though the RS and ZB clusters are not clearly separated as a mixed cluster is also found, a supervised machine learning model might be able to learn classification of the 82 octet binary materials.\n",
     "We might also expect that such model faces challenges especially when classifing materials in the transition area.\n",
     "A supervised learning algorithm, namely SISSO, has been used for such classification, and we resort to other tutorials in the AI toolkit to study this application.\n",
     "\n",