diff --git a/docs/source/examples/Bayes_tutorial-librascal.ipynb b/docs/source/examples/Bayes_tutorial-librascal.ipynb
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diff --git a/docs/source/examples/examples.rst b/docs/source/examples/examples.rst
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--- a/docs/source/examples/examples.rst
+++ b/docs/source/examples/examples.rst
@@ -12,3 +12,4 @@ the top-level :file:`examples/` directory.
    zundel_i-PI.ipynb
    Feature_selection_example.ipynb
    optimized_radial_basis_functions.ipynb
+   Bayes_tutorial-librascal.ipynb
diff --git a/examples/Bayes_tutorial-librascal.ipynb b/examples/Bayes_tutorial-librascal.ipynb
new file mode 100644
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+++ b/examples/Bayes_tutorial-librascal.ipynb
@@ -0,0 +1,36650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "62d08902",
+   "metadata": {},
+   "source": [
+    "# Using Bayesian optimisation for SOAP power spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb2089dd",
+   "metadata": {},
+   "source": [
+    "The task of this notebook is to demonstrate the performance of Bayesian optimization for SOAP Power Spectrum.  In this notebook we will solve the same problem as in the first example - build an interatomic potential for Zundel cations. To get the best result, we use the optimization process of 3 hyperparameters of the SOAP framework.\n",
+    "\n",
+    "When using SOAP, it is important to remember that a large number of parameters must be set in advance, before training. These are the thickness of the Gaussian blur, the number of radial and spherical features, the cutoff radius, and others. Such parameters that are not optimized during training and must be set before it starts are called *hyperparameters* [1]. Accordingly, there is a task to determine these parameters in such a way that the model gives the lowest error. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f71113d",
+   "metadata": {},
+   "source": [
+    "## Theoretical prerequisites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cdb6356",
+   "metadata": {},
+   "source": [
+    "Using simple search methods such as Grid Search, or Random Search, is not acceptable in such a case. Firstly, in order to check at least 5 possible points for each hyperparameter with the help of the Grid Search, we need to check 125 combinations, which is too many. Secondly, none of the methods guarantees that the result we get is at least somehow related to the real minimum. It is exactly the search for the optimal value, either by means of predetermined check points or by means of chance. Bayesian optimization presents an opportunity specifically for hyperparameter optimization, that is, it is a systematic method for determining the optimal set of hyperparameters. \n",
+    "\n",
+    "In general, Bayesian optimization belongs to the class of Sequential Model-based Global Optimization (SMBO) algorithms [2], the essence of which consists in the following. SMBO operates on a surrogate function, which is cheaper to work with than with function, that we should optimize (or it works well for the black-box functions, such as in our case). At each step, it optimizes this cheap function, gradually finding its maximum. Such a cheap function could be Expected Improvement (EI):\n",
+    "\n",
+    "$$\n",
+    "\\mathrm{EI}_{y^{*}}(x):=\\int_{-\\infty}^{\\infty} \\max \\left(y^{*}-y, 0\\right) p_{M}(y \\mid x) d y\n",
+    "$$\n",
+    "\n",
+    "This formula can be decomposed into two components. At each subsequent step, we search either for the point where there is a high probability of a minimum, or for the point where the variation of the parameter is large enough (because of the $\\max$ function, a large variation will lead exactly to a high estimate of the mean value). As a result, the algorithm will gradually learn more and more about the landscape of the function, finding the optimal value of the variable in passing. Such a surrogate function is also called an acquisition function because it defines the next point we would be interested in considering. \n",
+    "\n",
+    "But the next question arises, related to the fact that we know absolutely nothing about the function we are optimizing - how to estimate the values of a function at a point if we do not know what kind of function it is? This requires making prior over the function. The Gaussian process prior is most often used and is worth a closer look. \n",
+    "\n",
+    "A Gaussian process is essentially a generalization of the normal distribution on functions [3]. That is, a process will be called Gaussian if the functions in that process have a Gaussian distribution.  As we know, the normal distribution has two parameters - a vector of mean values (in case of functions it will be the mean function, respectively) and the covariance matrix (in case of functions it will be the covariance function). Accordingly, if we can gradually obtain information about these parameters, we will have more and more precise data about the function we are optimizing. Essentially, the Gaussian process is very similar to Kernel methods. We also compare a new unknown point with familiar samples, and try to predict the result depending on their similarity. Accordingly, it remains to be understood how to obtain the mean and covariance functions from known samples. \n",
+    "\n",
+    "Let $\\mathbf{f}$ be the function values we know, $\\mathbf{f}_{*}$ be the set of unknown values (which we are interested in evaluating), $\\mu, \\mu_{*}$ be the mean for known and unknown values respectively, $\\Sigma, \\Sigma_{*}, \\Sigma_{**}$ be the covariance matrix for known data, known-unknown and unknown data [4]. Then we can write that \n",
+    "\n",
+    "$$\n",
+    "\\mathbf{f}_{*} \\mid \\mathbf{f} \\sim \\mathcal{N}\\left(\\mu_{*}+\\Sigma_{*}^{\\top} \\Sigma^{-1}(\\mathbf{f}-\\boldsymbol{\\mu}), \\Sigma_{* *}-\\Sigma_{*}^{\\top} \\Sigma^{-1} \\Sigma_{*}\\right)\n",
+    "$$\n",
+    "\n",
+    "(this is simply the formula for conditioning a joint Gaussian distribution). This corresponds to a Gaussian process with mean and covariance:\n",
+    "\n",
+    "$$\n",
+    "m(x) = m(x)+\\Sigma(X, x)^{\\top} \\Sigma^{-1}(\\mathbf{f}-\\mathbf{m})\n",
+    "$$\n",
+    "$$\n",
+    "k_{GP}\\left(x, x^{\\prime}\\right) = k\\left(x, x^{\\prime}\\right)-\\Sigma(X, x)^{\\top} \\Sigma^{-1} \\Sigma\\left(X, x^{\\prime}\\right)\n",
+    "$$\n",
+    "\n",
+    "As you can see, the more points we have, the smaller covariance is, that is pretty reasonable"
+   ]
+  },
+  {
+   "attachments": {
+    "bayes.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "d3b48afc",
+   "metadata": {},
+   "source": [
+    "![bayes.png](attachment:bayes.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "acq_func.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "f1c0908f",
+   "metadata": {},
+   "source": [
+    "![acq_func.png](attachment:acq_func.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8a4f3c0",
+   "metadata": {},
+   "source": [
+    "Now let's spin all our considerations in the opposite direction in the form of an algorithm. We set some initial value of the covariance matrix (usually it is an RBF kernel), and calculate the function value at an arbitrary point from the specified range. From this, we can calculate the updated values of the mean and covariance. These values can then be substituted into the Expected Improvement function. It is already quite easy to optimize this function as we know its form and can use algorithms involving the first and second derivatives [5], [6]. Of course, this algorithm requires us to incur additional costs, we have to recalculate the mean and covariance, and optimize the surrogate function. But here we can recall that we specifically took a function whose optimization is a cheap procedure. Therefore, these intermediate calculations will not significantly increase the optimization time. Meanwhile, this algorithm will significantly reduce the number of calculations of the loss function, which is already much more expensive. As a result, we have obtained a way to systematically optimize hyperparameters.  \n",
+    "\n",
+    "[1]: Claesen M., De Moor B. Hyperparameter search in machine learning, arXiv preprint arXiv:1502.02127, 2015.\n",
+    "\n",
+    "[2]: Hutter F., Hoos H. H., Leyton-Brown K. Sequential model-based optimization for general algorithm configuration, International conference on learning and intelligent optimization, Springer, Berlin, Heidelberg, 507-523, 2011.\n",
+    "\n",
+    "[3]: Williams C. K. I., Rasmussen C. E. Gaussian processes for machine learning,  Cambridge, MA : MIT press, V. 2,  No 3, 4, 2006.\n",
+    "\n",
+    "[4]: Rasmussen C. E. Gaussian processes in machine learning, Summer school on machine learning,  Springer, Berlin, Heidelberg, 63-71, 2003.\n",
+    "\n",
+    "[5]: Frazier P. I. A tutorial on Bayesian optimization, arXiv preprint arXiv:1807.02811, 2018.\n",
+    "\n",
+    "[6]: Liu D. C., Nocedal J. On the limited memory BFGS method for large scale optimization, Mathematical programming, V. 45, No 1, 503-528, 1989"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81c63f68",
+   "metadata": {},
+   "source": [
+    "Let's move on to the practical use of Bayesian optimization. For this we will use the library *scikit-optimize*, (https://scikit-optimize.github.io/stable/index.html). It can be easily installed via ```pip```:\n",
+    "\n",
+    "```bash\n",
+    "$ pip install scikit-optimize\n",
+    "```\n",
+    "\n",
+    "This library itself has many options for optimization (including various acquisition functions), methods for analyzing results (including using graphs) and a lot of examples of using the library in practice. We will focus on a few of the simplest commands, which will get you started with the library. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4121ddaf",
+   "metadata": {},
+   "source": [
+    "The main part of the code repeats the first example, we will not dwell on it in too much detail, only pointing out the main blocks and what is directly relevant to the optimization task. First, import the required libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ac21f69d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-04T13:12:31.929306Z",
+     "iopub.status.busy": "2022-08-04T13:12:31.928826Z",
+     "iopub.status.idle": "2022-08-04T13:12:32.899761Z",
+     "shell.execute_reply": "2022-08-04T13:12:32.899079Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import ase\n",
+    "from ase.io import read, write\n",
+    "from ase.build import make_supercell\n",
+    "from ase.visualize import view\n",
+    "import numpy as np\n",
+    "# If installed -- not essential, though\n",
+    "try:\n",
+    "    from tqdm.notebook import tqdm\n",
+    "except ImportError:\n",
+    "    tqdm = (lambda i, **kwargs: i)\n",
+    "\n",
+    "from rascal.models import Kernel, train_gap_model, compute_KNM\n",
+    "from rascal.representations import SphericalInvariants\n",
+    "from rascal.utils import from_dict, to_dict, CURFilter, dump_obj, load_obj, get_score\n",
+    "\n",
+    "from skopt import Optimizer #<--- the key element, the object that conducts the optimization\n",
+    "from skopt.space import Real #<--- A class that allows us to define the space in which we will search for our \n",
+    "                             #     optimal set of hyperparameters\n",
+    "from skopt.plots import plot_convergence #<--- Allows easy graphing of algorithm convergence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de78910f",
+   "metadata": {},
+   "source": [
+    "And download our dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "33c0b47a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-04T13:12:32.904173Z",
+     "iopub.status.busy": "2022-08-04T13:12:32.903929Z",
+     "iopub.status.idle": "2022-08-04T13:12:32.909872Z",
+     "shell.execute_reply": "2022-08-04T13:12:32.909356Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/KliavinekSS/Documents/GitHub/librascal/examples/i-PI/zundel\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd i-PI/zundel/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c96ebd1",
+   "metadata": {},
+   "source": [
+    "In the snippets below we extract the relevant properties for each ASE frame. We load the total potential energies and we use the `ASE.Atoms.arrays` methods to get the atomic forces. For more information on how to use ASE-related methods, check the [ASE Atoms documentation](https://wiki.fysik.dtu.dk/ase/ase/atoms.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5909efbc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-04T13:12:32.913441Z",
+     "iopub.status.busy": "2022-08-04T13:12:32.913239Z",
+     "iopub.status.idle": "2022-08-04T13:12:33.154371Z",
+     "shell.execute_reply": "2022-08-04T13:12:33.153848Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['numbers', 'positions', 'forces'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the first N structures of the zundel dataset\n",
+    "N_dataset = 1000\n",
+    "frames = read('zundel_dataset.xyz', index=':{}'.format(N_dataset))\n",
+    "energies = np.loadtxt('zundel_energies.txt')[:N_dataset] \n",
+    "\n",
+    "#Keys of the arrays dictionary\n",
+    "print(frames[0].arrays.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5476786b",
+   "metadata": {},
+   "source": [
+    "Auxiliary function for selecting an array containing forces from a dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "08e9cd8b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-04T13:12:33.158070Z",
+     "iopub.status.busy": "2022-08-04T13:12:33.157863Z",
+     "iopub.status.idle": "2022-08-04T13:12:33.161457Z",
+     "shell.execute_reply": "2022-08-04T13:12:33.160966Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def extract_forces(frames,array_key='zeros'):\n",
+    "    f = []\n",
+    "    for frame in frames:\n",
+    "        if array_key is None:\n",
+    "            pass\n",
+    "        elif array_key == 'zeros':\n",
+    "            f.append(np.zeros(frame.get_positions().shape))\n",
+    "        else:\n",
+    "            f.append(frame.get_array(array_key))\n",
+    "    try:\n",
+    "        f = np.concatenate(f)\n",
+    "    except:\n",
+    "        pass\n",
+    "    return f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "126b2729",
+   "metadata": {},
+   "source": [
+    "## Build the GAP model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3104368f",
+   "metadata": {},
+   "source": [
+    "The preliminary step towards building a GAP model is defining a training and a test set. The training set is built by random selection of $800$ of the available structures, while the remaining $200$ will represent the test set. The given function will allow us to get information for train and test sets for each optimization iteration (each time we use a new train-test partition, so that we do not get an optimized description for a particular partition)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ba92c011",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-04T13:12:33.165240Z",
+     "iopub.status.busy": "2022-08-04T13:12:33.165072Z",
+     "iopub.status.idle": "2022-08-04T13:12:33.179971Z",
+     "shell.execute_reply": "2022-08-04T13:12:33.179474Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Number of structures to train the model with\n",
+    "n = 800\n",
+    "\n",
+    "np.random.seed(10)\n",
+    "# Split our data in the train and test blocks\n",
+    "def train_test():\n",
+    "    ids = list(range(N_dataset))\n",
+    "    np.random.shuffle(ids)\n",
+    "\n",
+    "    train_ids = [int(i) for i in ids[:n]]\n",
+    "    frames_train = [frames[ii] for ii in train_ids]\n",
+    "    y_train = [energies[ii] for ii in ids[:n]]\n",
+    "    y_train = np.array(y_train)\n",
+    "    f_train = extract_forces(frames_train, 'forces')\n",
+    "\n",
+    "    test_ids = [int(i) for i in ids[n:]] \n",
+    "    frames_test = [frames[ii] for ii in test_ids]\n",
+    "    y_train = [energies[ii] for ii in train_ids]\n",
+    "    y_train = np.array(y_train)\n",
+    "    y_test = [energies[ii] for ii in test_ids]\n",
+    "    y_test = np.array(y_test)\n",
+    "    f_test = extract_forces(frames_test, 'forces')\n",
+    "    return frames_train, y_train, f_train, frames_test, y_test, f_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9778967",
+   "metadata": {},
+   "source": [
+    "At this point, we also extract the distinct atomic species present in our system, define the atomic baseline energy and extract the information regarding energies and forces in the train and test set structures, which we will need later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b9995e8c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-04T13:12:33.183555Z",
+     "iopub.status.busy": "2022-08-04T13:12:33.183397Z",
+     "iopub.status.idle": "2022-08-04T13:12:33.186314Z",
+     "shell.execute_reply": "2022-08-04T13:12:33.185880Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "global_species = set()\n",
+    "for frame in frames:\n",
+    "    global_species.update(frame.get_atomic_numbers())\n",
+    "global_species = np.array(list(global_species))\n",
+    "\n",
+    "# Atomic energy baseline\n",
+    "atom_energy_baseline = np.mean(energies)/(frames[0].get_global_number_of_atoms())\n",
+    "energy_baseline = {int(species): atom_energy_baseline for species in global_species}\n",
+    "def fixed(number, digits):\n",
+    "    return f\"{number:.{digits}f}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a86ecdc",
+   "metadata": {},
+   "source": [
+    "Now we proceed with the actual calculation of the SOAP vectors of our training set. We need to specify an hyperparameters dictionary, which `librascal` uses to compute the structural features. The meaning of each hyperparameter is reported in [the `SphericalInvariants` documentation](https://lab-cosmo.github.io/librascal/reference/python.html#rascal.representations.SphericalInvariants). \n",
+    "\n",
+    "Since we have to redefine SOAP each iteration, we will also make it as a function. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d4a785a9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-04T13:12:33.190271Z",
+     "iopub.status.busy": "2022-08-04T13:12:33.190062Z",
+     "iopub.status.idle": "2022-08-04T13:12:33.194078Z",
+     "shell.execute_reply": "2022-08-04T13:12:33.193591Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def SOAP_definition(sigma, cutoff, width, compute_gradients):\n",
+    "    # define the parameters of the spherical expansion\n",
+    "    hypers = dict(soap_type=\"PowerSpectrum\",\n",
+    "                  interaction_cutoff=cutoff, \n",
+    "                  max_radial=6, \n",
+    "                  max_angular=4, \n",
+    "                  gaussian_sigma_constant=sigma,\n",
+    "                  gaussian_sigma_type=\"Constant\",\n",
+    "                  cutoff_function_type=\"RadialScaling\",\n",
+    "                  cutoff_smooth_width=width,\n",
+    "                  cutoff_function_parameters=\n",
+    "                        dict(\n",
+    "                                rate=1,\n",
+    "                                scale=3.5,\n",
+    "                                exponent=4\n",
+    "                            ),\n",
+    "                  radial_basis=\"GTO\",\n",
+    "                  normalize=True,\n",
+    "                  optimization=\n",
+    "                        dict(\n",
+    "                                Spline=dict(\n",
+    "                                   accuracy=1.0e-05\n",
+    "                                )\n",
+    "                            ),\n",
+    "                  compute_gradients=compute_gradients\n",
+    "                  )\n",
+    "\n",
+    "\n",
+    "    soap = SphericalInvariants(**hypers)\n",
+    "    return soap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f00c580",
+   "metadata": {},
+   "source": [
+    "The following snippet computes the descriptors and the managers for each frame, which will represent the expensive part of the calculation, in terms of memory usage.\n",
+    "\n",
+    "NOTE: the `librascal` structure manager only works if the atoms have been wrapped within the cell provided in the input file using e.g. `ase.Atoms.wrap()`, or one of the structure preprocessors provided in `rascal.neighbourlist.structure_manager` module (useful especially for non-periodic structures)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5576b0a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def soap_calculation(frames_train, sigma, cutoff, width):\n",
+    "    managers = []\n",
+    "    for f in frames_train:\n",
+    "        f.wrap(eps=1e-18)\n",
+    "\n",
+    "    soap = SOAP_definition(sigma, cutoff, width, False)\n",
+    "    managers = soap.transform(frames_train)\n",
+    "    return soap, managers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3197ebd8",
+   "metadata": {},
+   "source": [
+    "Next, we define a function to calculate the ideal sparse set, kernels to calculate, and create the model itself. You can read more about exactly how to do this in the first example in this documentation. We use the CUR method again, and use it to implement Sparse Kernel Ridge Regression (SparseKRR)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6000123a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_definition(soap, managers, sigma, cutoff, width, frames_train, f_train, y_train):\n",
+    "    n_sparse = {1:50, 8:100}\n",
+    "    compressor = CURFilter(soap, n_sparse, act_on='sample per species')\n",
+    "    X_sparse = compressor.select_and_filter(managers)\n",
+    "\n",
+    "    zeta = 2\n",
+    "\n",
+    "    soap = SOAP_definition(sigma, cutoff, width, True)\n",
+    "    kernel = Kernel(soap, name='GAP', zeta=zeta, target_type='Structure', kernel_type='Sparse')\n",
+    "\n",
+    "    KNM = compute_KNM(tqdm(frames_train, leave=True, desc=\"Computing kernel matrix\"), X_sparse, kernel, soap)\n",
+    "\n",
+    "    model = train_gap_model(kernel, frames_train, KNM, X_sparse, y_train, energy_baseline, \n",
+    "                            grad_train=-f_train, lambdas=[1e-12, 1e-12], jitter=1e-13)\n",
+    "    return soap, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cb434e6",
+   "metadata": {},
+   "source": [
+    "And a small function that will use our model to predict the properties of molecules. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9fa68eba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prediction(soap, model, frames_test):\n",
+    "    y_pred = []\n",
+    "\n",
+    "    for f in frames_test:\n",
+    "        positions = f.get_positions()\n",
+    "        f.set_positions(positions+[1,1,1])\n",
+    "        f.wrap(eps=1e-18)\n",
+    "        m = soap.transform(f)\n",
+    "        y_pred.append(model.predict(m))\n",
+    "\n",
+    "    y_pred = np.array(y_pred).flatten()\n",
+    "    return y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2884d7c2",
+   "metadata": {},
+   "source": [
+    "Let's move on to the most important thing, training and optimization. The key here will be the ```Optimizer``` class.  When you create an instance of a class, you can give it a lot of parameters, the main ones being an array of optimal value areas (in our case, they are\n",
+    "real numbers ```interaction_cutoff```, ```gaussian_sigma_constant``` and ```cutoff_smooth_width```) plus ```n_initial_points```. The latter is responsible for\n",
+    "how many times an instance of the optimizer class will just guess the next hyperparameter value. At ```n=1``` it will randomly choose 1 point, and after that it will already choose the points it thinks are optimal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cbc90eca",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-04T13:12:33.196640Z",
+     "iopub.status.busy": "2022-08-04T13:12:33.196450Z",
+     "iopub.status.idle": "2022-08-04T13:42:18.604053Z",
+     "shell.execute_reply": "2022-08-04T13:42:18.603499Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ab8170a37534856bb0302616b2cd6f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Number of iterations:   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9e420b96a2b456498fdac29052b9987",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Applications/anaconda3/lib/python3.7/site-packages/skcosmo/utils/_orthogonalizers.py:57: UserWarning: Column vector contains only zeros.\n",
+      "  warnings.warn(\"Column vector contains only zeros.\")\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46afd11689184fcd9272fa2ecf50185a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96aa2d0e170243b8aa671442b799d599",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4028ec9224a4c0bae01f0837ba3e818",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34b80f5905b44402a05837cded1b9cf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Applications/anaconda3/lib/python3.7/site-packages/skcosmo/utils/_orthogonalizers.py:57: UserWarning: Column vector contains only zeros.\n",
+      "  warnings.warn(\"Column vector contains only zeros.\")\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac59c7612ac34de1b43c31abdddac2a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad28e89fb14a4f9dbd9575e4742fa9e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "830bc8f843b0431a9d51440f4d0bd32f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Applications/anaconda3/lib/python3.7/site-packages/skopt/optimizer/optimizer.py:449: UserWarning: The objective has been evaluated at this point before.\n",
+      "  warnings.warn(\"The objective has been evaluated \"\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43b99853bf774bdba10965fcba3c7064",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "290d3e996f3e4899ba0e51b3b05b9826",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f55446f6e4b64d6a970a633e272025ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "564f5e6fe2d145ceadbee211a5f0bb5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d239d835043f442b9f6b9f8e111517dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing kernel matrix:   0%|          | 0/800 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "933625696b0441c48ddc4c494c5335f4",
+       "version_major": 2,
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+      ]
+     },
+     "metadata": {},
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+    },
+    {
+     "data": {
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+      ]
+     },
+     "metadata": {},
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal parameters: gaussian_sigma_constant=0.10, interaction_cutoff=3.57\n",
+      "\n",
+      "cutoff_smooth_width=0.65, RMSE = 10.55 meV\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create an instance of the Optimizer class\n",
+    "opt  = Optimizer([Real(0.1, 0.75), Real(2, 6), Real(0, 1)], n_initial_points=1)\n",
+    "\n",
+    "outfile = open('losses_information.txt', 'w')\n",
+    "# at each step we will record checked point and optimal one\n",
+    "outfile.write(f'sigma cutoff width loss sigma_opt cutoff_opt width_opt minimal_loss \\n')\n",
+    "outfile.close()\n",
+    "for _ in tqdm(range(100), desc=\"Number of iterations\"):\n",
+    "    suggested = opt.ask() # this function allows you to ask the optimizer which set of hyperparameters should be \n",
+    "                          # requested next\n",
+    "    # break it down into its individual components\n",
+    "    sigma, cutoff, width = suggested\n",
+    "    # define SOAP featurization and train-test splitting\n",
+    "    frames_train, y_train, f_train, frames_test, y_test, f_test = train_test()\n",
+    "    \n",
+    "    soap, managers = soap_calculation(frames_train, sigma, cutoff, width)\n",
+    "    # select the sparse points for the sparse kernel method with CUR on the whole training set, define a model\n",
+    "    soap, model = model_definition(soap, managers, sigma, cutoff, width, frames_train, f_train, y_train)\n",
+    "\n",
+    "    # make predictions on the test set\n",
+    "    y_pred = prediction(soap, model, frames_test)\n",
+    "    score = get_score(y_pred, y_test)\n",
+    "    RMSE = score['RMSE']\n",
+    "    \n",
+    "    res = opt.tell(suggested, RMSE) # <--- send the obtained result to the optimizer\n",
+    "    # write down our data to the file\n",
+    "    outfile = open('losses_information.txt', 'a')\n",
+    "    sigma_opt, cutoff_opt, width_opt, result = res.x[0], res.x[1], res.x[2], res.fun \n",
+    "    outfile.write(f'{fixed(sigma, 2)} {fixed(cutoff,2)} {fixed(width,2)} {fixed(RMSE, 4)} {fixed(sigma_opt, 2)}')\n",
+    "    outfile.write(f' {fixed(cutoff_opt,2)} {fixed(width_opt,2)} {fixed(result, 4)}\\n')\n",
+    "    outfile.close()  \n",
+    "    # repeat everything 100 times\n",
+    "print(f'Optimal parameters: gaussian_sigma_constant={fixed(res.x[0], 2)}, interaction_cutoff={fixed(res.x[1], 2)}\\n')\n",
+    "print(f'cutoff_smooth_width={fixed(res.x[2], 2)}, RMSE = {fixed(res.fun*1000, 2)} meV')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3f94dce2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-04T13:42:18.610809Z",
+     "iopub.status.busy": "2022-08-04T13:42:18.610582Z",
+     "iopub.status.idle": "2022-08-04T13:42:18.811708Z",
+     "shell.execute_reply": "2022-08-04T13:42:18.810993Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Convergence plot'}, xlabel='Number of calls $n$', ylabel='$\\\\min f(x)$ after $n$ calls'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# function for the convergence plotting\n",
+    "plot_convergence(res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9a6a5ff",
+   "metadata": {},
+   "source": [
+    "As you can see, this algorithm produces an optimal set of hyperparameters in 20 steps. The remaining iterations are carried out to make sure of the quality of the result obtained. At the same time, thanks to the cheapness of the acquisition function, this algorithm does not become too computationally heavy. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91345ba8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.7.3"
+  },
+  "widgets": {
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