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diff --git a/latest/_downloads/61809dd2f584c347a4fa27bcd9733a3c/ethanol_reduced_100.xyz b/latest/_downloads/61809dd2f584c347a4fa27bcd9733a3c/ethanol_reduced_100.xyz
diff --git a/latest/_downloads/8364416066e0abf7185cb42d53c97a9d/run_ase.py b/latest/_downloads/8364416066e0abf7185cb42d53c97a9d/run_ase.py
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+"""
+Running molecular dynamics with ASE
+===================================
+
+This tutorial demonstrates how to use an already trained and exported model to run an
+ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained
+using the :ref:`architecture-soap-bpnn` architecture on 100 ethanol systems
+containing energies and forces. You can obtain the :download:`dataset file
+<ethanol_reduced_100.xyz>` used in this example from our website. The dataset is a
+subset of the `rMD17 dataset
+<https://iopscience.iop.org/article/10.1088/2632-2153/abba6f/meta>`_.
+
+The model was trained using the following training options.
+
+.. literalinclude:: options.yaml
+   :language: yaml
+
+You can use the pretrained and exported :download:`model <exported-model.pt>`
+or train the model yourself with
+
+.. literalinclude:: train.sh
+   :language: bash
+
+A detailed step-by-step introduction on how to train a model is provided in
+the :ref:`label_basic_usage` tutorial.
+"""
+
+# %%
+#
+# First, we start by importing the necessary libraries, including the integration of ASE
+# calculators for metatensor atomistic models
+
+
+import ase.md
+import ase.md.velocitydistribution
+import ase.units
+import ase.visualize.plot
+import matplotlib.pyplot as plt
+import numpy as np
+import rascaline.torch  # noqa
+from ase.geometry.analysis import Analysis
+from metatensor.torch.atomistic.ase_calculator import MetatensorCalculator
+
+
+# %%
+#
+# .. note::
+#    We have to import ``rascaline.torch`` even though it is not used explicitly in this
+#    tutorial. The SOAP-BPNN model contains compiled extensions and therefore the import
+#    is required.
+#
+# Setting up the simulation
+# -------------------------
+#
+# Next, we initialize the simulation by extracting the initial positions from the
+# dataset file which we initially trained the model on.
+
+training_frames = ase.io.read("ethanol_reduced_100.xyz", ":")
+atoms = training_frames[0].copy()
+
+# %%
+#
+# Below we show the initial configuration of a single ethanol molecule in vacuum.
+
+ase.visualize.plot.plot_atoms(atoms)
+
+plt.xlabel("Å")
+plt.ylabel("Å")
+
+plt.show()
+
+
+# %%
+#
+# Our initial coordinates do not include velocities. We initialize the velocities
+# according to a Maxwell-Boltzmann Distribution at 300 K.
+
+ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300)
+
+# %%
+#
+# We now register our exported model as the energy calculator to obtain energies and
+# forces.
+
+atoms.calc = MetatensorCalculator("exported-model.pt")
+
+# %%
+#
+# Finally, we define the integrator which we use to obtain new positions and velocities
+# based on our energy calculator. We use a common timestep of 0.5 fs.
+
+integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs)
+
+
+# %%
+#
+# Run the simulation
+# ------------------
+#
+# We now have everything ready to run the MD simulation at constant energy (NVE). To
+# keep the execution time of this tutorial small we run the simulations only for 100
+# steps. If you want to run a longer simulation you can increase the ``n_steps``
+# variable.
+#
+# During the simulation loop we collect data about the simulation for later analysis.
+
+
+n_steps = 100
+
+potential_energy = np.zeros(n_steps)
+kinetic_energy = np.zeros(n_steps)
+total_energy = np.zeros(n_steps)
+trajectory = []
+
+for step in range(n_steps):
+    # run a single simulation step
+    integrator.run(1)
+
+    trajectory.append(atoms.copy())
+    potential_energy[step] = atoms.get_potential_energy()
+    kinetic_energy[step] = atoms.get_kinetic_energy()
+    total_energy[step] = atoms.get_total_energy()
+
+# %%
+#
+# Analyse the results
+# -------------------
+#
+# Energy conservation
+# ###################
+#
+# For a first analysis, we plot the evolution of the mean of the kinetic, potential, and
+# total energy which is an important measure for the stability of a simulation.
+#
+# As shown below we see that both the kinetic, potential, and total energy
+# fluctuate but the total energy is conserved over the length of the simulation.
+
+
+plt.plot(potential_energy - potential_energy.mean(), label="potential energy")
+plt.plot(kinetic_energy - kinetic_energy.mean(), label="kinetic energy")
+plt.plot(total_energy - total_energy.mean(), label="total energy")
+
+plt.xlabel("step")
+plt.ylabel("energy / kcal/mol")
+plt.legend()
+
+plt.show()
+
+# %%
+#
+# Inspect the systems
+# ###################
+#
+# Even though the total energy is conserved, we also have to verify that the ethanol
+# molecule is stable and the bonds did not break.
+
+animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None)
+plt.show()
+
+# %%
+#
+# Carbon-hydrogen radial distribution function
+# ############################################
+#
+# As a final analysis we also calculate and plot the carbon-hydrogen radial distribution
+# function (RDF) from the trajectory and compare this to the RDF from the training set.
+#
+# To use the RDF code from ase we first have to define a unit cell for our systems.
+# We choose a cubic one with a side length of 10 Å.
+
+for atoms in training_frames:
+    atoms.cell = 10 * np.ones(3)
+    atoms.pbc = True
+
+for atoms in trajectory:
+    atoms.cell = 10 * np.ones(3)
+    atoms.pbc = True
+
+# %%
+#
+# We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and
+# compute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf`
+# method.
+
+ana_traj = Analysis(trajectory)
+ana_train = Analysis(training_frames)
+
+rdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True)
+rdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True)
+
+# %%
+#
+# We extract the bin positions from the returned values and and averege the RDF over the
+# whole trajectory and dataset, respectively.
+
+bins = rdf_traj[0][1]
+rdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0)
+rdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0)
+
+# %%
+#
+# Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we
+# performed an energy-conserving and stable simulation.
+
+plt.plot(bins, rdf_traj_mean, label="trajectory")
+plt.plot(bins, rdf_train_mean, label="training set")
+
+plt.legend()
+plt.xlabel("r / Å")
+plt.ylabel("radial distribution function")
+
+plt.show()