diff --git a/docs/sphinx/requirements.txt b/docs/sphinx/requirements.txt
index e7be893c..3404388c 100644
--- a/docs/sphinx/requirements.txt
+++ b/docs/sphinx/requirements.txt
@@ -4,3 +4,5 @@ sphinxcontrib.bibtex
 sphinx-togglebutton
 sphinx-favicon
 pygments-lammps
+furo
+
diff --git a/docs/sphinx/source/journal-article.bib b/docs/sphinx/source/journal-article.bib
index 0c7ea46a..4ace726a 100644
--- a/docs/sphinx/source/journal-article.bib
+++ b/docs/sphinx/source/journal-article.bib
@@ -9,6 +9,16 @@ @article{schneider1978molecular
   publisher={APS}
 }
 
+@article{grossfield2009quantifying,
+  title={Quantifying uncertainty and sampling quality in biomolecular simulations},
+  author={Grossfield, Alan and Zuckerman, Daniel M},
+  journal={Annual Reports in Computational Chemistry},
+  volume={5},
+  pages={23--48},
+  year={2009},
+  publisher={Elsevier}
+}
+
 @book{hestenes1952methods,
   title={Methods of conjugate gradients for solving linear systems},
   author={Hestenes, Magnus Rudolph and Stiefel, Eduard and others},
diff --git a/docs/sphinx/source/non-tutorials/prerequisites.rst b/docs/sphinx/source/non-tutorials/prerequisites.rst
index 4cfc4f51..297b62ed 100644
--- a/docs/sphinx/source/non-tutorials/prerequisites.rst
+++ b/docs/sphinx/source/non-tutorials/prerequisites.rst
@@ -39,8 +39,8 @@ on statistical mechanics and molecular simulations.
 Software/system requirements
 ============================
 
-The LAMMPS stable release version 29Aug2024 (update2)
-and the matching LAMMPS--GUI software version 1.6.12 are required to
+The LAMMPS stable release version 22Jul2025
+and the matching LAMMPS--GUI software version 1.7.0 are required to
 follow the tutorials, as they include features that were first
 introduced in these versions.  For Linux (x86_64 CPU), macOS (BigSur or
 later), and Windows (10 and 11) you can download a precompiled LAMMPS
diff --git a/docs/sphinx/source/non-tutorials/using-lammps-gui.rst b/docs/sphinx/source/non-tutorials/using-lammps-gui.rst
index bcee45af..77d45f89 100644
--- a/docs/sphinx/source/non-tutorials/using-lammps-gui.rst
+++ b/docs/sphinx/source/non-tutorials/using-lammps-gui.rst
@@ -29,7 +29,7 @@ installer package.
 Installing the Linux .tar.gz Package
 ------------------------------------
 
-Download the archive (e.g., LAMMPS-Linux-x86_64-GUI-29Aug2024_update2.tar.gz)
+Download the archive (e.g., LAMMPS-Linux-x86_64-GUI-22Jul2025.tar.gz)
 and unpack it.  This will create a folder named LAMMPS--GUI containing the
 included commands, which can be launched directly using ``./lammps-gui`` or
 ``./lmp``, for example.  Adding this folder to the PATH environment
@@ -41,12 +41,12 @@ Installing the Linux Flatpak Bundle
 
 You have to have Flatpak support installed on Linux machine to be able
 to use the Flatpak bundle.  Download the bundle file
-(e.g., LAMMPS-Linux-x86_64-GUI-29Aug2024_update2.flatpak) and then
+(e.g., LAMMPS-Linux-x86_64-GUI-22Jul2025.flatpak) and then
 install it using the following command:
 
 .. code-block:: bash
 
-    flatpak install --user LAMMPS-Linux-x86_64-GUI-29Aug2024_update2.flatpak
+    flatpak install --user LAMMPS-Linux-x86_64-GUI-22Jul2025.flatpak
 
 This will integrate LAMMPS--GUI into your desktop environment
 (e.g., GNOME, KDE, XFCE) where it should appear in the ``Applications``
@@ -70,7 +70,7 @@ Installing the macOS Application Bundle
 ---------------------------------------
 
 After downloading the macOS app bundle image file
-(e.g., LAMMPS-macOS-multiarch-GUI-29Aug2024_update2.dmg), double-click
+(e.g., LAMMPS-macOS-multiarch-GUI-22Jul2025.dmg), double-click
 on it.  In the dialog that opens drag the LAMMPS--GUI app bundle into
 the Applications folder.  To enable command-line access, follow the
 instructions in the **README.txt** file.  These macOS app-bundles contain
@@ -87,7 +87,7 @@ Installing the Windows package
 ------------------------------
 
 Download the LAMMPS--GUI installer for Windows
-(e.g., LAMMPS-Win10-64bit-GUI-29Aug2024_update2.exe).  Windows may warn
+(e.g., LAMMPS-Win10-64bit-GUI-22Jul2025.exe).  Windows may warn
 you that the file is from an unknown developer and was downloaded from
 the internet.  This happens because neither the installer nor the
 LAMMPS--GUI application (or any other included applications) have been
diff --git a/docs/sphinx/source/shared/versionLAMMPS.rst b/docs/sphinx/source/shared/versionLAMMPS.rst
index e5de8c37..66076fc4 100644
--- a/docs/sphinx/source/shared/versionLAMMPS.rst
+++ b/docs/sphinx/source/shared/versionLAMMPS.rst
@@ -1,3 +1,3 @@
 .. container:: version
 
-    This tutorial is compatible with the 29Aug2024 (update 2) LAMMPS version.
+    This tutorial is compatible with the 22Jul2025 LAMMPS version.
diff --git a/docs/sphinx/source/tutorial1/tutorial.rst b/docs/sphinx/source/tutorial1/tutorial.rst
index 9cada2d6..bd296a2f 100644
--- a/docs/sphinx/source/tutorial1/tutorial.rst
+++ b/docs/sphinx/source/tutorial1/tutorial.rst
@@ -17,7 +17,7 @@ file **initial.lmp** in it, and open the file in the LAMMPS--GUI Editor window:
     # 1) Initialization
     # 2) System definition
     # 3) Settings
-    # 4) Visualization
+    # 4) Monitoring
     # 5) Run 
 
 .. admonition:: If you are not using LAMMPS-GUI
@@ -31,8 +31,8 @@ file **initial.lmp** in it, and open the file in the LAMMPS--GUI Editor window:
 Everything that appears after a hash symbol (#) is a comment
 and ignored by LAMMPS. These five categories are not required in every input script an do not
 necessarily need to be in that exact order.  For instance, the ``Settings``
-and the ``Visualization`` categories could be inverted, or
-the ``Visualization`` category could be omitted.  However, note that
+and the ``Monitoring`` categories could be inverted, or
+the ``Monitoring`` category could be omitted.  However, note that
 LAMMPS reads input files from top to bottom and processes each command
 *immediately*.  Therefore, the ``Initialization`` and
 ``System definition`` categories must appear at the top of the
@@ -61,6 +61,15 @@ so that the ``Initialization`` section appears as follows:
     atom_style atomic
     boundary p p p
 
+.. admonition:: Note
+    :class: non-title-info
+
+    Strictly speaking, none of the four commands specified in the
+    ``Initialization`` section are mandatory, as they correspond to the
+    default settings for their respective global properties.  However,
+    explicitly specifying these defaults is considered good practice to
+    avoid confusion when sharing input files with other LAMMPS users.
+
 The first line, ``units lj``, specifies the use of *reduced*  
 units, where all quantities are dimensionless.  This unit system is a  
 popular choice for simulations that explore general statistical  
@@ -86,19 +95,20 @@ slab geometries.
 .. admonition:: Note
     :class: non-title-info
 
-    Strictly speaking, none of the four commands specified in the
-    ``Initialization`` section are mandatory, as they correspond to the
-    default settings for their respective global properties.  However,
-    explicitly specifying these defaults is considered good practice to
-    avoid confusion when sharing input files with other LAMMPS users.
+    Each LAMMPS command is accompanied by extensive online |lammpsdocs|
+    that details the different options for that command.
+    From the LAMMPS--GUI editor buffer, you can access the documentation by  
+    right-clicking on a line containing a command (e.g., ``units lj``)  
+    and selecting ``View Documentation for `units'``.  This action  
+    should prompt your web browser to open the corresponding URL for the  
+    online manual.
 
-Each LAMMPS command is accompanied by extensive online |lammpsdocs|
-that details the different options for that command.
-From the LAMMPS--GUI editor buffer, you can access the documentation by  
-right-clicking on a line containing a command (e.g., ``units lj``)  
-and selecting ``View Documentation for `units'``.  This action  
-should prompt your web browser to open the corresponding URL for the  
-online manual.
+.. admonition:: Note
+    :class: non-title-info
+    
+    Most LAMMPS commands have default settings that are applied if no value
+    is explicitly specified.  The defaults for each command are listed at the
+    bottom of its documentation page.
 
 .. |lammpsdocs| raw:: html
 
@@ -121,6 +131,9 @@ The first line, ``region simbox (...)``, defines a region named
 from -20 to 20 units along all three spatial dimensions.  The second  
 line, ``create_box 2 simbox``, initializes a simulation box based  
 on the region ``simbox`` and reserves space for two types of atoms.
+In LAMMPS, an atom *type* represents a group of atoms that
+interact using the same set of force field parameters (here, the Lennard-Jones
+parameters).
 
 .. admonition:: Note
     :class: non-title-info
@@ -132,15 +145,29 @@ on the region ``simbox`` and reserves space for two types of atoms.
     terminate with an error.
 
 The third line, ``create_atoms (...)``, generates 1500 atoms of  
-type 1 at random positions within the ``simbox`` region.  The  
-integer 34134 is a seed for the internal random number generator, which  
+type 1 at random positions within the ``simbox`` region.
+The integer 34134 is a seed for the internal random number generator, which  
 can be changed to produce different sequences of random numbers and,  
 consequently, different initial atom positions.  The fourth line adds  
 100 atoms of type 2.  Both ``create_atoms`` commands use the  
 optional argument ``overlap 0.3``, which enforces a minimum  
 distance of 0.3 units between the randomly placed atoms.  This  
 constraint helps avoid close contacts between atoms, which can lead  
-to excessively large forces and simulation instability.
+to excessively large forces and simulation instability. Each atom created in
+LAMMPS is automatically assigned a unique atom ID, which serves as a numerical
+identifier to distinguish individual atoms throughout the simulation.
+Atom IDs by default have the range from 1 to the total number of atoms,
+but this is not enforced. Deleting atoms, for example, causes *holes* in the list
+of atom IDs.
+
+.. admonition:: Note
+    :class: non-title-info
+    
+    Another way to define a system in LAMMPS, besides the
+    ``create_atoms`` commands, is to import an existing topology
+    file containing atomic coordinates as well as, optionally, other
+    attributes such as atomic velocities and the force field parameters
+    using the ``read_data`` command, as shown in :ref:`carbon-nanotube-label`.
 
 .. include:: ../shared/needhelp.rst
 
@@ -189,19 +216,19 @@ of type 2, :math:`\epsilon_{22} = 0.5`, and :math:`\sigma_{22} = 3.0`.
     types using geometric average: :math:`\epsilon_{ij} = \sqrt{\epsilon_{ii} \epsilon_{jj}}`,
     :math:`\sigma_{ij} = \sqrt{\sigma_{ii} \sigma_{jj}}`.  In the present case,
     :math:`\epsilon_{12} = \sqrt{1.0 \times 0.5} = 0.707`, and
-    :math:`\sigma_{12} = \sqrt{1.0 \times 3.0} = 1.732`.
+    :math:`\sigma_{12} = \sqrt{1.0 \times 3.0} = 1.732`. Other rules
+    can be selected using the ``pair_modify`` command.
 
 Single-point energy
 -------------------
 
-The system is now fully parameterized, and the input is ready to compute
-forces.  Let us complete the two remaining categories,
-``Visualization`` and ``Run``, by adding the following lines
+The system is now fully parameterized.  Let us complete the two remaining categories,
+``Monitoring`` and ``Run``, by adding the following lines
 to **initial.lmp**:
 
 .. code-block:: lammps
 
-    # 4) Visualization
+    # 4) Monitoring
     thermo 10
     thermo_style custom step etotal press
     # 5) Run
@@ -216,6 +243,12 @@ The ``run 0 post no`` command instructs LAMMPS to initialize forces and energy
 without actually running the simulation.  The ``post no`` option disables
 the post-run summary and statistics output.
 
+.. admonition:: Note
+    :class: non-title-info
+
+    The *thermodynamic information* printed by LAMMPS refers to instantaneous values
+    of thermodynamic properties at the specified steps, not cumulative averages.
+
 You can now run LAMMPS (basic commands for running LAMMPS
 are provided in :ref:`running-lammps-label`.
 The simulation should finish quickly.
@@ -274,7 +307,7 @@ initially positive potential energy is expected, as the atoms are
 created at random positions within the simulation box, with some in very
 close proximity to each other.  This proximity results in a large
 initial potential energy due to the repulsive branch of the
-Lennard-Jones potential [i.e., the term in :math:`1/r^{12}` in
+Lennard-Jones potential [i.e., the term :math:`1/r^{12}` in
 Eq. :eq:`eq_LJ`].  As the energy minimization progresses, the energy
 decreases - first rapidly - then more gradually, before plateauing at a
 negative value.  This indicates that the atoms have moved to reasonable
@@ -297,7 +330,7 @@ following lines immediately after the ``minimize`` command:
 .. code-block:: lammps
 
     # PART B - MOLECULAR DYNAMICS
-    # 4) Visualization
+    # 4) Monitoring
     thermo 50
     thermo_style custom step temp etotal pe ke press
 
@@ -311,7 +344,7 @@ command is introduced to specify the thermodynamic information LAMMPS should
 print during ``PART B``.  This adjustment is made because, during
 molecular dynamics, the system exhibits a non-zero temperature :math:`T` (and
 consequently a non-zero kinetic energy :math:`K`, keyword ``ke``), which are useful to monitor.
-The ``pe`` keyword represents the potential energy of the system, :math:`E`, such that
+The ``pe`` keyword represents the potential energy of the system, :math:`U`, such that
 :math:`U + K = E`.
 
 Then, add a second ``Run`` category by including the following
@@ -325,7 +358,8 @@ lines in ``PART B`` of **initial.lmp**:
     run 50000
 
 The ``fix nve`` command updates the positions and velocities of the
-atoms in the group ``all`` at every step.  The group ``all``
+atoms in the group ``all`` at every step.  More specifically, this command integrates
+Newton's equations of motion using the velocity-Verlet algorithm.  The group ``all``
 is a default group that contains all atoms.  The last two lines specify
 the value of the ``timestep`` and the number of steps for the
 ``run``, respectively, for a total duration of 250 time units.
@@ -333,10 +367,10 @@ the value of the ``timestep`` and the number of steps for the
 .. admonition:: Note
     :class: non-title-info
 
-    Since no other fix commands alter forces or velocities, and periodic
-    boundary conditions are applied in all directions, the MD simulation
-    will be performed in the microcanonical (NVE) ensemble, which
-    maintains a constant number of particles and a fixed box volume.  In
+    Since the only command affecting forces and velocities in the
+    present script is ``fix nve``, and periodic boundary conditions are applied
+    in all directions, the MD simulation will be performed in the microcanonical (NVE) ensemble, which
+    maintains a constant number of particles and a fixed box volume. In
     this ensemble, the system does not exchange energy with anything
     outside the simulation box.
 
@@ -387,6 +421,14 @@ it reaches a plateau value of about -0.25.  The kinetic energy,
 increases rapidly during molecular dynamics until it reaches  
 a plateau value of about 1.5.
 
+.. admonition:: Note
+    :class: non-title-info
+
+    All simulations presented in these tutorials are deliberately kept 
+    short so they can be executed on a personal computer.  These runs are not intended 
+    to produce statistically meaningful results, and should not be considered suitable 
+    for publication (see for instance Ref. :cite:`grossfield2009quantifying`).
+
 From the information  
 printed in the ``Output`` window, one can see that the temperature  
 starts from 0 but rapidly reaches the requested value and  
@@ -423,7 +465,7 @@ thermodynamic information.  To better follow the evolution of the system
 and visualize the trajectories of the atoms, let us print the positions
 of the atoms in a file at a regular interval.
 
-Add the following command to the ``Visualization`` section  
+Add the following command to the ``Monitoring`` section  
 of ``PART B`` of the **initial.lmp** file:
 
 .. code-block:: lammps
@@ -440,7 +482,7 @@ and adjust the visualization to your liking using the available buttons.
 Now you can copy the commands used to create this visualization to the  
 clipboard by either using the ``Ctrl-D`` keyboard shortcut or  
 selecting ``Copy dump image command`` from the ``File`` menu.  
-This text can be pasted into the ``Visualization`` section  
+This text can be pasted into the ``Monitoring`` section  
 of ``PART B`` of the **initial.lmp** file.  This may look like  
 the following:
 
@@ -496,7 +538,7 @@ commands in the ``System definition`` section:
     pair_style lj/cut 4.0
     pair_coeff 1 1 1.0 1.0
     pair_coeff 2 2 0.5 3.0
-    # 4) Visualization
+    # 4) Monitoring
     thermo 10
     thermo_style custom step etotal press
     # 5) Run
@@ -549,7 +591,7 @@ right-clicking on the file name in the editor and selecting the entry
 The created **.data** file contains all the information necessary  
 to restart the simulation, such as the number of atoms, the box size,  
 the masses, and the pair coefficients.  This **.data** file also  
-contains the final positions of the atoms within the ``Atoms``  
+contains the final positions of the atoms, along with their IDs and types, within the ``Atoms``  
 section.  The first five columns of the ``Atoms`` section  
 correspond (from left to right) to the atom indexes (from 1 to the total  
 number of atoms, 1150), the atom types (1 or 2 here), and the positions  
@@ -591,7 +633,7 @@ and **improved.min.lmp**:
     boundary p p p
     # 2) System definition
     # 3) Settings
-    # 4) Visualization
+    # 4) Monitoring
     # 5) Run
 
 Since we read most of the information from the data file, we don't need  
@@ -664,6 +706,14 @@ The equal-style ``variables`` are expressions evaluated
 during the run and return a number.  Here, they are defined to count  
 the number of atoms of a specific group within the ``cyl_in`` region.
 
+.. admonition:: Note
+    :class: non-title-info
+        
+    The ``n1_in`` and ``n2_in`` defined above are
+    equal-style variables, which evaluate a numerical expression using the
+    ``count()`` function.  Other common LAMMPS variable types include
+    atom-style, index, and loop.
+
 In addition to counting the atoms in each region, we will also extract  
 the coordination number of type 2 atoms around type 1 atoms.  The  
 coordination number measures the number of type 2 atoms near  
@@ -680,11 +730,23 @@ atoms.  Add the following lines to **improved.md.lmp**:
 
 The ``compute reduce ave`` command is used to average the per-atom  
 coordination number calculated by the ``coord/atom``  
-compute command.  Compute commands are not automatically invoked; they  
+compute command.  Compute commands do not print or output
+anything by themselves, nor are they automatically executed; they  
 require a *consumer* command that references the compute.  In this case, the  
 first compute is referenced by the second, and we reference the second  
 in a ``thermo_style custom`` command (see below).
 
+.. admonition:: Note
+    :class: non-title-info
+
+    LAMMPS ``compute`` commands can produce three kinds of data: scalars (single values), 
+    vectors (one-dimensional arrays), or arrays (two-dimensional tables).  
+    When referencing results of a compute, you can use indices: for example, 
+    ``c\_mycompute`` refers to the entire scalar, vector, or array, and
+    ``c\_mycompute[1]`` refers to its first element (in case of vector or array).  
+    In general, *consumer* commands can only work with certain data types,
+    check the documentation of each command to ensure compatibility.
+
 .. admonition:: Note
     :class: non-title-info
 
@@ -696,7 +758,7 @@ Finally, let us complete the script by adding the following lines to
 
 .. code-block:: lammps
 
-    # 4) Visualization
+    # 4) Monitoring
     thermo 1000
     thermo_style custom step temp pe ke etotal press v_n1_in v_n2_in c_sumcoor12
     dump viz all image 1000 myimage-*.ppm type type shiny 0.1 box no 0.01 view 0 0 zoom 1.8 fsaa yes size 800 800
@@ -781,7 +843,7 @@ expected during mixing.  This can be observed using the entry
 
 ..  container:: figurelegend
 
-    Figure: a) Evolution of the numbers :math:`N_\text{1, in}$` and :math:`N_\text{2, in}` of atoms
+    Figure: a) Evolution of the numbers :math:`N_\text{1, in}` and :math:`N_\text{2, in}` of atoms
     of types 1 and 2, respectively, within the ``cyl_in`` region as functions
     of time :math:`t`.  b) Evolution of the coordination number :math:`C_{1-2}`
     (compute ``sumcoor12``) between atoms of types 1 and 2.
diff --git a/docs/sphinx/source/tutorial2/introduction.rst b/docs/sphinx/source/tutorial2/introduction.rst
index d340f2bd..36800dac 100644
--- a/docs/sphinx/source/tutorial2/introduction.rst
+++ b/docs/sphinx/source/tutorial2/introduction.rst
@@ -16,6 +16,7 @@ by imposing a constant velocity on the edge atoms.  To illustrate the
 difference between conventional and reactive force fields, this tutorial
 is divided into two parts: in the first part, a conventional molecular
 force field (called OPLS-AA :cite:`jorgensenDevelopmentTestingOPLS1996`)
-is used and the bonds between the atoms of the CNT are unbreakable.  In
+is used and the form of the bonded potential ensure that the bonds between the
+atoms of the CNT are unbreakable.  In
 the second part, a reactive force field (called AIREBO :cite:`stuart2000reactive`)
 is used, which allows chemical bonds to break under large strain.
diff --git a/docs/sphinx/source/tutorial2/tutorial.rst b/docs/sphinx/source/tutorial2/tutorial.rst
index 516b33a9..06d55885 100644
--- a/docs/sphinx/source/tutorial2/tutorial.rst
+++ b/docs/sphinx/source/tutorial2/tutorial.rst
@@ -3,7 +3,9 @@ Unbreakable bonds
 
 With most conventional molecular force fields, the chemical bonds between
 atoms are defined at the start of the simulation and remain fixed, regardless
-of the forces applied to the atoms.  These bonds are typically modeled as springs
+of the forces applied to the atoms.  In this tutorial, these bonds are
+explicitly specified in the **.data** file, which is read using the ``read_data`` command (see below).
+Bonds are typically modeled as springs
 with equilibrium distances :math:`r_0` and force constants :math:`k_\text{b}`:
 :math:`U_\text{b} = k_\text{b} \left( r - r_0 \right)^2`.  Additionally, angular and
 dihedral constraints are often imposed to preserve the molecular structure
@@ -48,7 +50,7 @@ the scripts required for the second part of the tutorial.
     file in a text editor of your choice, and copy the previous lines into it.
 
 The chosen unit system is ``real`` (therefore distances are in
-Ångströms (Å), times in femtoseconds (fs), and energies in kcal/mol), the
+Ångströms (Å), times in femtoseconds (fs), and energies in (kcal/mol)), the
 ``atom_style`` is ``molecular`` (therefore atoms are point
 particles that can form bonds with each other), and the boundary
 conditions are fixed.  The boundary conditions do not matter here, as
@@ -64,8 +66,8 @@ potential.
 The ``bond_style``, ``angle_style``, ``dihedral_style``, and ``improper_style``
 commands specify the different potentials used to constrain the relative
 positions of the atoms.  The ``special_bonds`` command sets the weighting factors
-for the Lennard-Jones interactions between atoms directly connected by
-one bond, two bonds, and three bonds, respectively.  This is done for
+for the Lennard-Jones interactions between atoms sitting one,
+two, or three bonds away from each other, respectively. This is done for
 convenience when parameterizing the force constants for bonds, angles, and
 so on.  By excluding the non-bonded (Lennard-Jones) interactions for
 these pairs, those interactions do not need to be considered when determining
@@ -160,6 +162,13 @@ corresponding to the following: :math:`x < x_\text{min}` (``rbot``, for region
 bottom), :math:`x_\text{min} > x > x_\text{max}` (``rmid``, for region middle),  
 and :math:`x > x_\text{max}` (``rtop``, for region top).
 
+.. admonition:: Note
+    :class: non-title-info
+
+    So far, variables have been referenced dynamically during the run using
+    the ``v_`` prefix, which evaluates the variable as it evolves over time.
+    Here, a dollar sign ($) is used to reference the variable at the time the script is read.
+
 Finally, let us define 3 groups of atoms corresponding to the atoms
 in each of the 3 regions by adding to **unbreakable.lmp**
 just before the ``run 0`` command:
@@ -174,7 +183,9 @@ just before the ``run 0`` command:
     set group cnt_mid mol 3
 
 With the three ``set`` commands, we assign unique, otherwise unused
-molecule IDs to atoms in those three groups.  We will use this IDs later to
+molecule IDs to atoms in those three groups.  A molecule ID is an
+integer that groups atoms into a *molecule* for bookkeeping purposes, and can be
+useful for tracking and post-processing.  We will use this IDs later to
 assign different colors to these groups of atoms.
 
 Run the simulation using LAMMPS.  The number of atoms in each group is given in
@@ -235,6 +246,14 @@ The ``fix nve`` commands are applied to the atoms of ``cnt_top`` and
 from these groups are recalculated at every step.  The ``fix nvt`` does the  
 same for the ``cnt_mid`` group, while also applying a Nosé-Hoover thermostat  
 with desired temperature of 300 K :cite:`nose1984unified, hoover1985canonical`.  
+
+.. admonition:: Note
+    :class: non-title-info
+
+    The Nosé-Hoover thermostat only controls the temperature of
+    the atoms belonging to the specified ``cnt_mid`` group.  Atoms outside
+    this group are not affected by the thermostat.
+
 To restrain the motion of the atoms at the edges, let us add the following  
 commands to **unbreakable.lmp**:
 
@@ -248,7 +267,10 @@ commands to **unbreakable.lmp**:
 The two ``setforce`` commands cancel the forces applied on the atoms of the  
 two edges, respectively.  The cancellation of the forces is done at every step,  
 and along all 3 directions of space, :math:`x`, :math:`y`, and :math:`z`, due to the use of  
-``0 0 0``.  The two ``velocity`` commands set the initial velocities  
+``0 0 0``.  Although the forces on these atoms is set to zero,
+the ``fix`` still stores the forces acting on the group before
+cancellation, which can later be extracted for analysis (see below).
+The two ``velocity`` commands set the initial velocities  
 along :math:`x`, :math:`y`, and :math:`z` to 0 for the atoms of ``cnt_top`` and  
 ``cnt_bot``, respectively.  As a consequence of these last four commands,  
 the atoms of the edges will remain immobile during the simulation (or at least  
@@ -357,6 +379,7 @@ from the typical dependency of bond energy with bond distance,
     The CNT starts deforming at :math:`t = 5\,\text{ps}`, and :math:`L_\text{cnt-0}` is the  
     CNT initial length.  b) Evolution of the total energy :math:`E` of the system with time :math:`t`.  
     Here, the potential is OPLS-AA, and the CNT is unbreakable.
+    The orange line shows the raw data, and the blue line represents a time-averaged curve.
 
 Importing YAML log file into Python
 -----------------------------------
@@ -400,6 +423,7 @@ the **unbreakable.yaml** file can then be used to plot the stress-strain curve.
     as a function of the strain, :math:`\Delta L_\text{cnt} = (L_\text{cnt}-L_\text{cnt-0})/L_\text{cnt-0}`,
     where :math:`L_\text{cnt}` is the CNT length and :math:`L_\text{cnt-0}` the CNT initial length.
     Here, the potential is OPLS-AA, and the CNT is unbreakable.
+    The orange line shows the raw data, and the blue line represents a time-averaged curve.
 
 Breakable bonds
 ===============
@@ -565,7 +589,8 @@ curve reveals a linear (elastic) regime where
     where :math:`F_\text{cnt}` is the force and :math:`A_\text{cnt}` the CNT surface area,
     as a function of the strain, :math:`\Delta L_\text{cnt} = (L_\text{cnt}-L_\text{cnt-0}/L_\text{cnt-0})`, where
     :math:`L_\text{cnt}` is the CNT length and :math:`L_\text{cnt-0}` the CNT initial length.
-    Here, the potential is AIREBO, and the CNT is breakable.
+    Here, the potential is AIREBO, and the CNT is breakable.  The orange line shows
+    the raw data, and the blue line represents a time-averaged curve.
 
 Tip: bonds representation with AIREBO
 -------------------------------------
diff --git a/docs/sphinx/source/tutorial3/figures/PEG-density-dm.png b/docs/sphinx/source/tutorial3/figures/PEG-density-dm.png
index dd42e7cf..60819093 100644
Binary files a/docs/sphinx/source/tutorial3/figures/PEG-density-dm.png and b/docs/sphinx/source/tutorial3/figures/PEG-density-dm.png differ
diff --git a/docs/sphinx/source/tutorial3/figures/PEG-density.png b/docs/sphinx/source/tutorial3/figures/PEG-density.png
index 3d2baa4c..ad1ebbfc 100644
Binary files a/docs/sphinx/source/tutorial3/figures/PEG-density.png and b/docs/sphinx/source/tutorial3/figures/PEG-density.png differ
diff --git a/docs/sphinx/source/tutorial3/figures/water.ipynb b/docs/sphinx/source/tutorial3/figures/water.ipynb
index e81c1ee4..1b442f69 100644
--- a/docs/sphinx/source/tutorial3/figures/water.ipynb
+++ b/docs/sphinx/source/tutorial3/figures/water.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "id": "7c8c9669",
    "metadata": {},
    "outputs": [],
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "id": "a28c02aa",
    "metadata": {},
    "outputs": [],
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "id": "2f5b9386",
    "metadata": {},
    "outputs": [],
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "id": "5f6dbb52",
    "metadata": {},
    "outputs": [],
@@ -78,13 +78,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 17,
    "id": "88e6cdea",
    "metadata": {},
    "outputs": [
     {
      "data": {
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FO+LtfR/RSNgnXXLBmbrwrO6/j4if8iZ9+VaTiVjP4ePSV39R8g5gEtIgvh8fNrt3715S/nPjxtoocvUaLRaLKRqNNv0c1Wu06sd3evxq/NwBwHDKFaR3f1t6eI/z8fe8RPq55d/OAQA6rNX34pT6bKBc/sTtdqtUKimXy2l+fl7FYlEej6epoN/icVaqumdfdby20+Mvp9WyoOVds930/PPPt3W8gw46qOE5yWRy1c/rlhTaf2vqfLuoeKG158znpT3L9Fc77rjjGo4xMzPT1u9xLBaTyyW99wLpV75e23/p+aR05X15Tbf5/7Vcunc5+/btW9HXGtx/c3LOodJHL6lfMiOTydQ8p0fSaqqofenGvJ7KSL92Zv3A2a5du5ZcTFqtkZERbZ+Wfv9KcxHayeZgSsH552VlpXijAQvS7iYym6pLITvp9u+IB3ZJ37s3qU35ZZ63YIK87fsfMN+Ldn+tTr8jvG7plC3m9v9eLP3r12b0zJPte96sO9bwHI+db/73YbmUaIPTOvk7YiVs22RH/8d1GW1LPd/4NSNJqeV/H5V57HzDoWKFXQqWmvgJLUiz+6TZBqctVzK9LJVKtf37+/M/X9TJm6X7di69P1+U/u426YqXDe77iMUav2pW9j5iWQXpn289TnsC0s+fVL+iQLvfRxR9Mf2oQc9Uj0s6+1DTe7QfFkTFYrGpv1dllOXvLcuylqyX6gXLqteJrfwfS7VrvOo1VKfHBwAMP9uW/uSG+kG/151A0A8ABlE/rHP7WjKZXFLCZbFisag9e/bI6/UqFAotuzuyOpDW7sbpnR5/Oa3UlpXMwjgWa+byE9a6jSPSb5wt/eUttcd++Lh0Wven1HbrotIHX9n9Umi2pH//mbnQ/fHLpPXNbw5fsfmc2UVYLyvp6HXS6zZKP76x83Pppbt2SB/+vnRwh8pX9iOnnnBYnT3zprzm7c9K67n+vyoul/Su86Tf+GZtFvUNT5pylVi5Qkn6m1ulHz0l/eHFpldpp1kNfr8eu156/0XOWfa9kk6nlUqtoP41emLx+sfj8dRdB1aX/mx1s2b1+dV93Ds9PgBg+P3Hz+q3JHnRQdLvndfd+QAA2qMPOnn1t1zO1AfyeDzy+/0KBoM1OyELhYLm5+e1e/fuyvmNtDtDr9vjoz8Ner8zJ68/UTrOodpccUiKFK+P9vb/7f5d0tv/y/To6rT7Xqgf9Dt+g/Rnr+2v3l+dcPN26YPf7UxPrbXK45JGQyaYcNIm6fzDpVcdt7RX5jAp2dK3HpDe9nUT9OuUT1wm/f5LpAu2SSPN95oeWEetk15ZJ8H9rxw2n6B1D+02f2++drcJBvZC2Ce9+3zpy2/or6AfBotlWcrnD2RFL7ehsXrd2GqmZnUGX3Wgr9PjAwCG24+3S//wU+djW+LSJ182mCXRAQBk/DUUjUYVDocdM+iqm6nbtq2ZmRmNj48rEGjvVbLq5293Rl+nx0d7+T3SwaPmQnflNi6NuKQv/1WvZ9deHrfZlf+Ob6puLzSsTjIrffB70i+dIr3z7Pr9HlfLqvP/d9Im6fOvGv6ssGsfkz57/fAErXtpLCR97S3SaFCKBJyD5zt2SP/U3lZwDbOIOu25hPT5G03AvtM2xqQzDjabL0q29NSUdPcL0t3PS/fulDJDmGX4jrOkG56Q0lVf2+P7pIN7M6WOGQ9Jm0aqbjEp5pK+3t52hkvki9JXbpNu2p/9t3Wic89V7SVbTdBvXRcy3DHcZmdnKx/7/f5l+6q73W65XK5KdZZWS3EuPt+pf0enxwcADK8n9kmf+qHzsahf+twrTX9wAMBgIvDXwHLlO0dGRhQIBDQzs7SJSCKRcGzuvhqrzeDr5PgjIyMt7Q7tRe+IZnrptCIeb9xJKR6PN/28zyVMGcRqbpcU8B64fe6VJsi3MebcJ2dhwdP2r3W510DZ+Ph4W583HA4v+XzbhPSW06R/u+vAfUWXX0nvQXK5zK59fxt+rJa7cFO2bt26uscSaWnXvKmRX8/o/out5UDFcuMtntdqv7+2Lc2kTXlAW+b7V+2/7pXu3yl94mXS5hFp06ZNTf2sL2chb36+S7aU9ozXHD9ti/TZV0qh/b9CotHosl9roWh6PC40aD+2PipNRkwJv2Z+53T6d8SVD0p/dtPSMoJZT1xJr3nekaDZUdnp7E+PZ3h+Rxw8uvw5fr9/2ecs2eY1O5U2P1fN+NNbQrq8IL3+hPqB6mZe061Yt26dCkXpG/dK/3KnCZxUs1yhJT9LG2OrD+AvvgDsdklHrjO3XzrFfL8e2yd9/1ubNJeKq1AymVTroqt73vHx2t8R1Rr9jliJ8u+I8bD0K2dIf/uT2nMW/AfpiMn6Pepa1e73EZKUtaQds84/I2URv/TBi6QLj3A+3o73EcWStDdl/uZIUt5d+zvi0b1mQ8+vnG7+vrf6O8K2zaaV3fPOm4Ly7gPvI9ZFTfbqeYe3/KV0VTgcbmnjoGVZLZfbx+olk0kVCiZt3+PxaGysce3aQCCwpHWEZVlNr50WZxbW+/no9PgAgOEzvSB96PvOlWg8LtPjuhvl2QEAneOyq5vDoWWJRKKmD+Do6OiSIEJ1dmA8Hq8JbiynUCho3759lc+DweCShWanx18sn89renq68vnExAQ7RFfpF/5V2lPV1uX4jdKXf84ELiDlCqZE2HOztcfOOFj601f37nuVK0h/cbP03Yfrn+N1m0yD1xzf2//TR/dKV1wj7VzmWmHUL33gYunCbat7rrt2mMVErk5Zy7MOkT51uQlqt6JkS1/7mfTVO8zH9Zx+sPTRS8zF/F76+t3S395W//jLj5E+cJH5GUH3WUXpmsdMb4vlXheLxQLSz59kbrEO74J9fJ/0JzdIT0wtf954SHrPBat/3a51VtGUUX0hWXvszadJv3VO9+fUiG1L33nIlCRdLuh3zHrpisukzavbz9G0+3ZKn7vB+Xu52JGT0gcvNuVWm/FC0vS3vGvH8ue5XdIbTpLefuZwZpTzfrz70um0kknzA+3xeDQ5OdlUlZRMJrMkSzAWizW1aUaSdu06kOK9YcMGx+fr9PiL8XMHAIMvV5De823poT3Ox999vnkPBQDoL62+F+cyYxs47dpevHtSqi2d2WqGXXV8tnoXZ6fHR+fM52qDfpK5EEbQ74CA15T8dHLnDunax7s7n7Ldc9K7vrV80G9dRPrr10uvPaH3/6fHrJf+8Reli+pke0hSKi99/AfSl35cP2jXyO3PSn/4vfqPP/cw6dOvaD3oJ5mLuW89Xfrz15msvnru2iH9+n+Z0oS9YNvSP/50+aDf60805e4I+vWOz2N6Av7HW0yguJmdrfM56Z/vlH7h30zZwkS6/fPKFaS/v036zf9uHPR7xTHSv7+ZoF87+DzS/3ux87H/vrdxEKvbFvLSFdeaQNhyQb+fP0n665/rXtBPkk7eLP3zL0m/eLK03J++J6ak3/wf8/tyua+hUDQB+l/5euOg31HrpK/8vPSu84Yz6IfuS6VSlaCfy+XS+Ph4060RqitKpNPN/dHIZDKVj4PBYN3n6/T4AIDhYdvSn/6oftDvNcdLP3diV6cEAOgQSn22QXVvBak28Fa9kGq1/0L1eNWl6zo9Pjpn+7Tz/du62PdmUJy82bwR/c5Dtcf++haTQTbauFpn29y5Q/qja025sXpO3WIyLMZ6nHW2WDRg5nTaluUzRL71gPTgLlPmo1FZxcVueVr6xA/q9/S7YJv08UtXX4rwlM3SP/2S9KnrpDuecz5nJi39wXekXz1DeuuL2lemr5GSbX4m/+f++uf88mnSb5zd+2AwDK9buuxo6ZKjTJP7f7+rccAtbUlfu9vc/B7J7zX/BrxSYP+//v0f+/eXbK4cX3x/1XFbprTxjtnln39jzGyIOGPYGtD12IsPMxnD1cElqyR9+VazaaEfPLZPuuIH0gvLZXAHpA9dLJ2/tXvzWizok373PFNa9HM3mNLPTool8zN/83azGeLYDUuPP7hb+sKN0tMzzo8vC3mlt58l/dxJbKhAe5RKJSWTyUp1F6/Xq7GxMXm9rS2jY7GY5ufnJZl1Wi6Xa1haM5U6sDMwFov1dHwAwHD4z3tMtRMnp26R3nM+61MAGBYE/trE4/FU+j1ItYG46gy6VjPyqs+vXmx2enx0zlN1An9bCfw5+q1zpFuflqarNjMnsyaI9bFLOz+HQslc6P/nBqUm33SqCez048VHl8tkIJ6wUfrENc4lVCUT+HjHN6X3XShdelTjcX/0pPTJHzr3XJKklx4pfeSS9n1PRkPS518l/efd0ld/KhUd/j9KtvRPd5iycx+9RJpYJkuwHYol6Qs/kr7/SP1z3nm29Msv6uw8sDJul8mcu2CryVz9t7vq74hdLF9cPlupnVySfv7k4S1h2Gsul/SuF5uM4erfKTc/Lf1sh/SiHgZbbVv6vwdMELLeBgtJOm6D2eSxcaR7c6vnhI3SV3/R9Kn8+j31/3Y+PSP99v9KbzxV+rUzzGvq728zG34a9SY49zBzsaofvl4Mh0wmo2QyWdncGQ6HV9z7OBqNKp1OVzZnJpPJZUuFptPpytoyHo83XJt1enwAwOC75WnzvsrJlhHpj14ueckBAIChQY+/Npmenl5S3rO6t0KpVNKePQeuHHo8Hq1fv77p8VOpVGUXpyRt2rRpyfFOj78YvR3a6ws3Slc5lIm8+jekCN9WRzdvlz5ytfOxz79KOvvQzj33w7tNaYwn6wRsJSnkkz700sEpu5fOm7Ke9Xb+lb3yWFPvP1inEvAPH5c+c51z8E0yvew+eFHnsu7u3yl98lpp30L9c2IBkxl6xsEmo2ddcy1wmmYVpU/9ULrxqfrn/P5LTIlPDAbblu5+wQQA73mh17ORDhszPThP2NjrmQy/L/3YZD5XO3xc+uov9WZTx3zO9Hz88fblz/ulU8wGg9VmVnfCo3ulz17fOHvv4FFTynSmQdXCibD07peYYP1a2qHO+/HOSafTSqV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",
       "text/plain": [
        "<Figure size 1800x700 with 2 Axes>"
       ]
@@ -94,7 +94,7 @@
     },
     {
      "data": {
-      "image/png": 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/nDp1qsDRAAAAACh2rPUWBtZ6ADB/7PgDAGAeIpFI/LXD4chL0/WHHnpI0vmG5ywEz2tubo7/3ft8vgJHAwAAAKDYsdZbGFjrAcD8kfgDAGAeDMOIv3Y6nXn5zMnFjsvlysvnFYvJpvcsBgEAAABkirXewsFaDwDmh8QfAABpmlr2RcrPYjAUCsU/t6WlJeefV0wmn4gNhUJ5678BAAAAoPSw1ltYWOsBwPyQ+AMAIE3TFxxr167N+WcePnw4/rrcG71PN3UxzpOgAAAAAOaLtd7CwloPAOaHxB8AAGma2vPBbrfnpedDf3+/pPyVmikmVqs1/v+DYDBY4GgAAAAAFCvWegsLaz0AmJ8lhQ4AAIBi89RTT8Vf22y2nH+eYRjxJ08zbfQeDofV29srn8+no0ePym63J4z5fD4Fg0FFIhEZhiGr1aq6ujo1Nzdr06ZN81r4+v1+BQIBRSIRRSIRhcNhWa1W2Ww22Ww2NTQ0yOl0JsSSrvXr16u3tzepNA8AAAAApIq1XnpY6wHAwkTiDwCANE19CjQfpVgGBgbir9NdfIZCIQ0MDGhwcFD9/f0JpWui0Wj8dSAQ0JYtW5KuNwxDwWBQwWBQu3fvltvtVkdHR0qf7fP55PV644u/uro6rV+/XjabTZFIJN7Lore3V5LSuvd0DQ0N8fsEAoGMF80AAAAAyg9rPdZ6AFAKSPwBAJAiv9+v/fv3Jzxp2N3drd7eXo2NjckwDElSZ2enWltbs/a5U0uapLP49Hg88nq9c77P7/erra0tpXt6vV4Fg0E98MADsz4R2t7eru7ubkkz/32EQiF5PJ6slGyZ2nsjGAyyGAQAAACQMtZ6rPUAoJSQ+AMAYA4ej0fd3d3xxd5UhmEknc92A/ipT26mUyKltbVVLS0tGh0dVTAYNF0Y+nw+7d69Ww6HQ62trXI6naqsrNTY2JjC4bD8fn98UTcpFArp6quv1rFjx0w/d+o1HR0dMy6MHQ6Henp6tHnzZgWDQVVVVaX8Z5tu6tOxU/++AAAAAGAmrPVY6wFAKaqIxWKxQgcBAMBCNvWpz4GBAe3evTt+fPTo0aRFTLZLwjQ2NsZjOHXq1LzvU1NTk7BwdTqdCgaDcz61ahiGrr766qSeCi6XS/v27Ut6f319fXxB9vjjj8+5gA2Hw6qvr8/46dlVq1ZJOv/3P9NCFQAAAAAmsdZjrQcApWhRoQMAAGChczgc8f+mn29oaEgYz0UfiMk+E/Nptj7V9J4RwWBQXV1dcy7ArFarjh07lrSo6+7uTnricmpzekmqrKycM65MGr2b3WdqXw4AAAAAmAlrPdZ6AFCKSPwBAJCGwcHB+Gun05mXz5x8cjOVhdVspl/vcrnU3Nyc8vV79+5NOufz+RKOpy/Epjarn01HR0fWyuaYlekBAAAAgNmw1kvEWg8AiheJPwAA0jB1MZjt/g5mpi5sLBZLVu+dzkJw8v3Tn3Kd3qx9eozbt29PqQ+D2+3O+AnabP/9AAAAACgfrPVY6wFAqSDxBwBAGqb2PshFqZfZZPoU6HTzabA+vVTM9Kc+p5dyMQxD9fX12rZtm/x+f06f0Jz698OToAAAAADSwVqPtR4AlIolhQ4AAIBiMf1pxmz1K5jN2NhY/PVCeMpx+pOvZosul8ul7u7uhHO9vb3q7e2VdP7vbf369aqrq5PT6czJ3+PY2FjGfTIAAAAAlAfWeqz1AKCUkPgDACBFU58AzVfPh6lPNkaj0bx85mymN403W8jt27dPkUgkqTTMpHA4rHA4HF8w2u12ud3uORvPz2XqwjnbT8wCAAAAKF2s9VjrAUApodQnAAApCgQC8df5Lv0iJS52Forpi8NJPT096uzsTOlJzHA4rN27d6uxsTGjsi1TF8s8AQoAAAAgVaz1krHWA4DiReIPAIAU5bvZu5S4qFkIT4FOX5A2NDTM+N7W1lYNDw/r6NGjcrlcc5Z5CYVC8ng8WYsNAAAAAFLBWo+1HgCUEkp9AgCQokI2e5cWxmJneu+LpqamOa9paGiILxoNw1AwGFQgEDBtAN/d3a2Ojo55PcU5eS+eAAUAAACQDtZ6rPUAoJSw4w8AgBQUotn7pMmFZyalUbJlai8Hh8OR9t+D1WpVc3Oz9u3bp+HhYXV1dSW9Z2BgIKMYZypJAwAAAADTsdY7j7UeAJQOEn8AAKRg6hOg+VwISgtrcdPb2xt/vXfvXtP3bNu2LeHvazbNzc3q6OhIODefMjdTF8oL6e8LAAAAwMLGWu881noAUDpI/AEAkIKpTybW1tbm9bOnLj6nP42aTz6fL/75TqfTtOeDYRjq7e1NK87pJWTms9iORCIZXQ8AAACgPLHWY60HAKWGxB8AACmYWvbEbLHR3t6e8pOP6ZraXD5Xn5GK2267Lf66s7Nz1vem06Ni6lOfVqt1Xj01pi4+nU5n2tcDAAAAKE+s9VjrAUCpIfEHAEAKZir/EgqFVF9fr0gkkrMm8FMXN5n2RJiqra0t5ac1t23bJsMwZLVadfTo0RmftJxcBAYCgZTjePDBB+OvZyopM5epfy9mT6cCANkbSuUAAAT4SURBVAAAgBnWeqz1AKDUkPgDAGAO0xdMg4ODCoVCam9vV2Njo1pbW9XT05Ozz7darfHFVzafAg2Hw9qwYYN8Pt+s72lsbFRvb6+ampr0xBNPzLrYmvy76u3tld/vnzOGUCgkr9cr6fyit7W1Nc0/xe/uIylnC3IAAAAApYe1Hms9AChFSwodAAAAC930BVh3d7e6u7vjT0Tm46nD9evXKxwOJ5ShyQbDMLR7927ddtttWr9+vS6//HJZLBZFo1EFAgEFg0FZrVZ1dXWpubl5zvtN7b/Q1tYml8ulnTt3Jj01ahiG9u/fH18INjU16dChQ/P+c0z+vVD6BQAAAECqWOux1gOAUkTiDwCAOVgslqRzTU1NuvPOO2W1WvMSwzXXXKPu7m5J5xen2X7acbJRe29vb8L5dP+c05+YnVw4S+fL5lgsFkUiERmGET+3d+/elBaaM5laama+T5ECAAAAKD+s9VjrAUApIvEHAMAcGhoa5HQ6NTg4qLq6Ornd7rz3FnA4HLLb7QqHw3rwwQezshg8duyYwuGwfD6fIpGIxsbGZBiG7Ha7amtr1dramvafs6OjQ62trQoGgwoEAopEIvHFXzgcltVqlc1mk9PplNPpzMrf42SZmcm/IwAAAABIBWu91LHWA4DiURGLxWKFDgIAAMzN6/XK4/HIarVqeHg47es3b96cUD7m2LFjJdEnoaamRoZhqLOzk6dAAQAAABQd1nrmWOsBwPwsKnQAAAAgNS6XS9L5Ui1TS56Us0AgEC8ls2nTpgJHAwAAAADpY62XjLUeAMwfiT8AAIqE1WqNLwgnG6WXu8m/B7fbnbceHAAAAACQTaz1krHWA4D5I/EHAEAR6ejokCQFg8Gk5urlJhQKKRgMymq16vrrry90OAAAAAAwb6z1foe1HgBkhsQfAABFxGq1qrOzU5Lk8XgKHE1hTf759+7dyxOgAAAAAIoaa73fYa0HAJkh8QcAQJFpbW2Vw+FQb29v2fZ/8Pv9CgaDcjqdNHkHAAAAUBJY67HWA4BsIPEHAEAReuCBB2S1WrV9+/ZCh5J3hmGovb1dVqtVXV1dhQ4HAAAAALKGtR5rPQDIFIk/AACKkNVq1cGDB2UYhrZt21bocPJq165dMgwjviAGAAAAgFLBWo+1HgBkisQfAABFqqGhQV1dXert7U2pB8TY2Fjug8oxj8ej3t5edXV1yeFwFDocAAAAAMg61nqs9QAgEyT+AAAoYs3Nzers7JTX65XP55v1vdFoNOE4HA7nMrSs8/l88nq96urqUnNzc6HDAQAAAICcYa0HAJivJYUOAAAAZKa1tVU2m03bt2/Xpk2bEkqiGIahsbExBYPBpMWfz+eT3W6XzWZb8GVUDMOQ1+vVsWPHePoTAAAAQFlgrQcAmI+KWCwWK3QQAAAg+9rb29Xd3Z3y+4eHhxf8ohAAAAAAyh1rPQDAbNjxBwBAiWpublZdXZ0qKyslSRaLJWF8shzMZD8IFoIAAAAAsPCx1gMAzIYdfwAAAAAAAAAAAEAJWFToAAAAAAAAAAAAAABkjsQfAAAAAAAAAAAAUAJI/AEAAAAAAAAAAAAlgMQfAAAAAAAAAAAAUAJI/AEAAAAAAAAAAAAlgMQfAAAAAAAAAAAAUAJI/AEAAAAAAAAAAAAlgMQfAAAAAAAAAAAAUAJI/AEAAAAAAAAAAAAl4P8Hx/WKNDPnXGUAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1800x700 with 2 Axes>"
       ]
@@ -130,16 +130,16 @@
     "\n",
     "    # Panel b\n",
     "    myplt.add_panel()\n",
-    "    myplt.add_plot(x = time, y = rho, type = \"plot\", linewidth_data = 3,\n",
+    "    myplt.add_plot(x = time, y = rho/1000, type = \"plot\", linewidth_data = 3, # todo: remove the /1000 when the log will be updated\n",
     "                marker = \"-\", data_color = color2, markersize = 12)\n",
     "    #x = np.linspace(0, 15)\n",
     "    #myplt.add_plot(x = x, y = x*0+996, type = \"plot\", linewidth_data = 1.5,\n",
     "    #               marker = \"--\", data_color = color4, markersize = 12)\n",
-    "    myplt.complete_panel(ylabel = r'$\\rho ~ (\\mathrm{kg/m}^3)$',\n",
+    "    myplt.complete_panel(ylabel = r'$\\rho ~ (\\mathrm{g/cm}^3)$',\n",
     "                        xlabel = r'$t~\\mathrm{(ps)}$',\n",
     "                        xpad = 10, legend=True, handlelength_legend=1)\n",
-    "    myplt.set_boundaries(x_ticks=np.arange(0, 16, 5), y_ticks=np.arange(300, 1100, 200),\n",
-    "                        x_boundaries=(-1, 16.2), y_boundaries=(350, 1020))\n",
+    "    myplt.set_boundaries(x_ticks=np.arange(0, 16, 5), y_ticks=np.arange(0.300, 1.100, 0.200),\n",
+    "                        x_boundaries=(-1, 16.2), y_boundaries=(0.350, 1.020))\n",
     "\n",
     "    # Print figure\n",
     "    myplt.add_subplotlabels()\n",
diff --git a/docs/sphinx/source/tutorial3/introduction.rst b/docs/sphinx/source/tutorial3/introduction.rst
index a8f5b0cf..d99cd86e 100644
--- a/docs/sphinx/source/tutorial3/introduction.rst
+++ b/docs/sphinx/source/tutorial3/introduction.rst
@@ -25,4 +25,14 @@ solver :cite:`luty1996calculating`.
 This tutorial was inspired by a
 publication by Liese and coworkers, in which molecular dynamics
 simulations are compared with force spectroscopy experiments, see
-Ref. :cite:`liese2017hydration`.
\ No newline at end of file
+Ref. :cite:`liese2017hydration`.
+
+.. admonition:: Note
+    :class: non-title-info
+
+    When mixing different force fields, as is done here with GROMOS 
+    and SPC/Fw, users should exercise caution.  The choices made in these tutorials 
+    prioritize progressive learning of LAMMPS functionality 
+    over strict physical accuracy.  While GROMOS is commonly used with water 
+    models from the SPC family :cite:`oostenbrink2004biomolecular`, 
+    the inter-compatibility of force fields is not generally guaranteed.
diff --git a/docs/sphinx/source/tutorial3/tutorial.rst b/docs/sphinx/source/tutorial3/tutorial.rst
index 68b889fc..4e63a57a 100644
--- a/docs/sphinx/source/tutorial3/tutorial.rst
+++ b/docs/sphinx/source/tutorial3/tutorial.rst
@@ -35,7 +35,7 @@ angles, and dihedrals used in the simulation, here ``harmonic``.
 With the ``pair_style`` named ``lj/cut/coul/long``, atoms
 interact through both a Lennard-Jones (LJ) potential and Coulomb
 interactions.  The value of :math:`10\,\text{Å}` is the cutoff, and the
-``ewald`` command defines the long-range solver for the Coulomb
+``kspace_style`` command defines the long-range solver for the Coulomb
 interactions :cite:`ewald1921berechnung`.  Finally, the
 ``special_bonds`` command, which was already seen in
 :ref:`carbon-nanotube-label`, sets the LJ and Coulomb
@@ -151,7 +151,8 @@ Add the following line into **water.lmp**:
 The ``fix npt`` allows us to impose both a temperature of :math:`300\,\text{K}`
 (with a damping constant of :math:`100\,\text{fs}`), and a pressure of 1 atmosphere
 (with a damping constant of :math:`1000\,\text{fs}`).  With the ``iso`` keyword,
-the three dimensions of the box will be re-scaled simultaneously.
+the three dimensions of the box will be re-scaled isotropically,
+maintaining the same proportion in all directions.
 
 Let us output the system into images by adding the following commands to **water.lmp**:
 
@@ -163,30 +164,15 @@ Let us output the system into images by adding the following commands to **water
     acolor OW red acolor HW white &
     adiam OW 3 adiam HW 1.5
 
-Let us also extract the volume and density every 500 steps:
+Let us also extract the volume and density, among others, every 500 steps:
 
 .. code-block:: lammps
 
-    variable myvol equal vol
-    variable myoxy equal count(H2O)/3
-    variable NA equal 6.022e23
-    variable Atom equal 1e-10
-    variable M equal 0.018
-    variable rho equal ${myoxy}*${M}/(v_myvol*${NA}*${Atom}^3)
     thermo 500
-    thermo_style custom step temp etotal v_myvol v_rho
+    thermo_style custom step temp etotal volume density
 
-Here, several variables are defined and used for converting the units of the
-density in kg/mol:  The variable ``myoxy`` represents the number of
-atoms divided by 3,  which corresponds to the number of molecules, :math:`N_\text{H2O}`,
-and the variable ``myrho`` is the density in kg/mol:  
-
-.. math::
-
-    \rho = \dfrac{N_\text{H2O}}{V N_\text{A}},
-
-where :math:`V` is the volume in :math:`\text{m}^3`, :math:`N_\text{A}` the Avogadro number, and
-:math:`M = 0.018`\,kg/mol the molar mass of water.
+With the real units system, the volume is in :math:`Å^3`, and
+the density is in :math:`\text{g/cm}^3`.
 
 Finally, let us set the timestep to 1.0 fs, and run the simulation for 15 ps by
 adding the following lines into **water.lmp**:
@@ -315,10 +301,15 @@ the position :math:`(0, 0, 0)`:
 
     fix myrct PEG recenter 0 0 0 shift all
 
-Note that the ``recenter`` command has no impact on the dynamics,
-it simply repositions the frame of reference so that any drift of the
-system is ignored, which can be convenient for visualizing and analyzing
-the system.
+.. admonition:: Note
+    :class: non-title-info
+
+    Note that the ``recenter`` command has no impact on the dynamics,
+    it simply repositions the frame of reference so that any drift of the
+    system is ignored, which can be convenient for visualizing and analyzing
+    the system. However, be aware that using ``fix recenter`` can sometimes
+    mask underlying issues in the simulation, such as net momentum or the so-called
+    *flying ice cube syndrome* :cite:`wong2016good`.
 
 Let us create images of the systems:
 
@@ -418,7 +409,6 @@ constraints using the following commands:
 .. code-block:: lammps
         
     compute rgyr PEG gyration
-    compute prop PEG property/local dtype
     compute dphi PEG dihedral/local phi
 
 The radius of gyration can be directly printed with the ``thermo_style`` command:
@@ -427,7 +417,7 @@ The radius of gyration can be directly printed with the ``thermo_style`` command
 
     thermo_style custom step temp etotal c_rgyr
     thermo 250
-    dump mydmp all local 100 pull.dat index c_dphi c_prop
+    dump mydmp all local 100 pull.dat index c_dphi
 
 By contrast with the radius of gyration (compute ``rgyr``), the dihedral angle
 :math:`\phi` (compute ``dphi``) is returned as a vector by the ``compute dihedral/local``
@@ -514,11 +504,13 @@ named **pull.lammpstrj**, which can be opened in OVITO or VMD.
 .. admonition:: Note
     :class: non-title-info
 
-    Since the trajectory dump file does not contain information about
+    Since the default trajectory dump file does not contain information about
     topology and elements, it is usually preferred to first write out a
     data file and import it directly (in the case of OVITO) or convert it
     to a PSF file (for VMD).  This allows the topology to be loaded before
     *adding* the trajectory file to it.  When using LAMMPS--GUI,
     this process can be automated through the ``View in OVITO`` or
     ``View in VMD`` options in the ``Run`` menu.  Afterwards
-    only the trajectory dump needs to be added.
\ No newline at end of file
+    only the trajectory dump needs to be added.  Alternatively, the
+    ``dump custom`` command can be combined with ``dump`` command to
+    include element names in the dump file and simplify visualization.
diff --git a/docs/sphinx/source/tutorial4/tutorial.rst b/docs/sphinx/source/tutorial4/tutorial.rst
index d0e07504..b73b825b 100644
--- a/docs/sphinx/source/tutorial4/tutorial.rst
+++ b/docs/sphinx/source/tutorial4/tutorial.rst
@@ -32,9 +32,13 @@ file in a text editor of your choice, and copy the following into it:
     The editor should display the following content corresponding to **create.lmp**
 
 These lines are used to define the most basic parameters, including the
-atom, bond, and angle styles, as well as interaction
+atom, bond, and angle styles, as well as the non-bonded interaction
 potential.  Here, ``lj/cut/tip4p/long`` imposes a Lennard-Jones potential with
 a cut-off at :math:`12\,\text{Å}` and a long-range Coulomb potential.
+The parameters ``O``, ``H``, ``O-H``, and ``H-O-H`` correspond
+respectively to the oxygens, hydrogens, O-H bonds, and H-O-H angle constraints of
+the water molecules; their definitions, provided by the ``labelmap`` commands,
+will be clarified below.
 
 .. include:: ../shared/needhelp.rst
 
@@ -168,7 +172,6 @@ must be located next to **create.lmp**. The **parameters.inc** file contains the
     mass Cl- 35.453
     mass WALL 26.9815
 
-
 Each ``mass`` command assigns a mass in g/mol to an atom type.
 The **parameters.inc** file also contains the pair coefficients:
 
@@ -190,10 +193,10 @@ types.  By default, LAMMPS calculates the pair coefficients for the
 interactions between atoms of different types (i and j) by using
 geometric average: :math:`\epsilon_{ij} = \sqrt{\epsilon_{ii} \epsilon_{jj}}`,
 :math:`\sigma_{ij} = \sqrt{\sigma_{ii} \sigma_{jj}}`.  However, if the default
-value of :math:`1.472\,\text{kcal/mol}` was used for :math:`\epsilon_\text{1-5}`,
+value of :math:`1.472\,\text{kcal/mol}` was used for :math:`\epsilon_\text{O-WALL}`,
 the solid walls would be extremely hydrophilic, causing the water
 molecules to form dense layers.  As a comparison, the water-water energy
-:math:`\epsilon_\text{1-1}` is only :math:`0.185199\,\text{kcal/mol}`.  Therefore,
+:math:`\epsilon_\text{O-O}` is only :math:`0.185199\,\text{kcal/mol}`.  Therefore,
 to make the walls less hydrophilic, the value of
 :math:`\epsilon_\text{O-WALL}` was reduced.
 
@@ -214,7 +217,7 @@ the force constant of the angular harmonic potential to 0 and the equilibrium
 angle to :math:`104.52^\circ`.
 
 Alongside **parameters.inc**, the **groups.inc** file contains
-several ``group`` commands to selects atoms based on their types:
+several ``group`` commands to define groups of atoms based on their types:
 
 .. code-block:: lammps
 
@@ -331,6 +334,16 @@ used to apply a restraint force when used during minimization.  This last keywor
 here, because the spring constants of the rigid water molecules were set
 to 0 (see the **parameters.inc** file).
 
+.. admonition:: Note
+    :class: non-title-info
+
+    LAMMPS provides several ways to maintain molecules rigid during a simulation. 
+    The ``fix shake`` command is appropriate for constraining bond lengths 
+    and angles within small molecules like water. 
+    However, it may fail for linear molecules like :math:`\text{CO}_2` or more complex rigid bodies. 
+    In such cases, the ``fix rigid`` family of commands can be used instead to
+    treat entire molecules or groups of atoms as rigid bodies.
+
 Let us also create images of the system and control
 the printing of thermodynamic outputs by adding the following lines
 to **equilibrate.lmp**:
@@ -344,7 +357,7 @@ to **equilibrate.lmp**:
     thermo 1
     thermo_style custom step temp etotal press
 
-Let us perform an energy minization by adding the following lines to **equilibrate.lmp**:
+Let us perform an energy minimization by adding the following lines to **equilibrate.lmp**:
 
 .. code-block:: lammps
 
@@ -358,7 +371,7 @@ images of the system, you will notice that the atoms and molecules are moving to
 System equilibration
 --------------------
 
-Let us equilibrate further the entire system by letting both fluid and piston
+Let us equilibrate further the entire system by letting both fluid and wall
 relax at ambient temperature.  Here, the commands are written within the same
 **equilibrate.lmp** file, right after the ``reset_timestep`` command.
 
@@ -387,6 +400,14 @@ for the trajectory visualization:
 The ``undump`` command is used to cancel the previous ``dump`` command.
 Then, a new ``dump`` command with a larger dumping period is used.
 
+.. admonition:: Note
+    :class: non-title-info
+        
+    Just like the ``undump`` command can cancel an active ``dump``, other
+    objects defined in a LAMMPS input script can be cancelled when no longer needed. 
+    For example, you can use ``unfix`` to remove a previously defined ``fix``, and
+    ``uncompute`` to delete a ``compute``.
+
 To monitor the system equilibration, let us print the distance between
 the two walls.  Add the following lines to **equilibrate.lmp**:
 
@@ -443,7 +464,8 @@ the end of the simulation.
 
     Figure: a) Pressure, :math:`p`, of the nanosheared electrolyte system as a function
     of the time, :math:`t`.  b) Distance between the walls, :math:`\Delta z`, as a
-    function of :math:`t`.
+    function of :math:`t`. The orange line shows the raw data, and the blue line
+    represents a time-averaged curve.
 
 Imposed shearing
 ----------------
@@ -513,7 +535,10 @@ The ``setforce`` commands cancel the forces on ``walltop`` and
 ``wallbot``.  As a result, the atoms in these two groups will not
 experience any forces from the rest of the system.  Consequently, in the absence of
 external forces, these atoms will conserve the initial velocities imposed by the
-two ``velocity`` commands.
+two ``velocity`` commands.  As seen previously, although the
+forces on these atoms are set to zero, the ``fix setforce`` still stores the
+forces acting on the group before cancellation, which can later be extracted
+for analysis (see below).
 
 Finally, let us generate images of the systems and print the values of the
 forces exerted by the fluid on the walls, as given by ``f_mysf1[1]``
@@ -530,8 +555,9 @@ and ``f_mysf2[1]``.  Add these lines to **shearing.lmp**:
     thermo_style custom step temp etotal f_mysf1[1] f_mysf2[1]
 
 Let us also extract the density and velocity profiles using
-the ``chunk/atom`` and ``ave/chunk`` commands.  These commands are
-used to divide the system into bins and return the desired quantities, here the velocity
+the ``chunk/atom`` and ``ave/chunk`` commands.  These
+commands discretize the simulation domain into spatial bins and compute and output
+average properties of the atoms belonging to each bin, here the velocity
 along :math:`x` (``vx``) within the bins.  Add the following lines to **shearing.lmp**:
 
 .. code-block:: lammps
diff --git a/docs/sphinx/source/tutorial5/tutorial.rst b/docs/sphinx/source/tutorial5/tutorial.rst
index 5c95b023..da9b5f02 100644
--- a/docs/sphinx/source/tutorial5/tutorial.rst
+++ b/docs/sphinx/source/tutorial5/tutorial.rst
@@ -30,8 +30,9 @@ and a **.data** file is imported by the ``read_data`` command.
 .. include:: ../shared/needhelp.rst
 
 The initial topology given by |silica_data_5|
-is a small amorphous silica structure.  This structure was created using a force field called
-Vashishta :cite:`vashishta1990interaction`.  If you open the **silica.data**
+is a small amorphous silica structure.  This structure was generated in a prior
+simulation using the Vashishta force field :cite:`vashishta1990interaction`.
+If you open the **silica.data**
 file, you will find in the ``Atoms`` section that all silicon atoms have a
 charge of :math:`q = 1.1\,\text{e}`, and all oxygen atoms have a charge of :math:`q = -0.55\,\text{e}`.
 
@@ -52,7 +53,7 @@ Next, copy the following three crucial lines into the **relax.lmp** file:
 .. code-block:: lammps
 
     pair_style reaxff NULL safezone 3.0 mincap 150
-    pair_coeff * * reaxCHOFe.inc Si O
+    pair_coeff * * ffield.reax.CHOFe Si O
     fix myqeq all qeq/reaxff 1 0.0 10.0 1.0e-6 reaxff maxiter 400
 
 In this case, the ``pair_style reaxff`` is used without a control file.  The
@@ -62,13 +63,25 @@ allocation issues, which sometimes can trigger segmentation faults and
 file, which should have been downloaded during the tutorial set up.  Finally, the
 ``fix qeq/reaxff`` is used to perform charge equilibration :cite:`rappe1991charge`,
 which occurs at every step.  The values 0.0 and 10.0 represent the
-low and the high cutoffs, respectively, and :math:`1.0 \text{e} -6` is the tolerance.
+low and the high cutoffs, respectively, and :math:`1.0 \text{e} -6` is the tolerance,
+i.e., the precision to which the atomic charges are equilibrated during the
+charge equilibration process.
+
+.. admonition:: Note
+    :class: non-title-info
+        
+    The ``pair_style reaxff`` command optionally accepts a control file,
+    which defines control variables such as
+    global parameters of the ReaxFF potential, as well as performance and output settings. 
+    If no control file is provided, as in this tutorial, LAMMPS uses its default values,
+    which correspond to those in Adri van Duin's original stand-alone ReaxFF code :cite:`van2001reaxff`.
+
 The ``maxiter`` sets an upper limit to the number of attempts to
 equilibrate the charge.
 
 .. |reaxCHOFe_inc_5| raw:: html
 
-    <a href="https://raw.githubusercontent.com/lammpstutorials/lammpstutorials-inputs/refs/heads/main/tutorial5/reaxCHOFe.inc" target="_blank">reaxCHOFe.inc</a>
+    <a href="https://raw.githubusercontent.com/lammpstutorials/lammpstutorials-inputs/refs/heads/main/tutorial5/ffield.reax.CHOFe" target="_blank">ffield.reax.CHOFe</a>
 
 Next, add the following commands to the **relax.lmp** file to track the
 evolution of the charges during the simulation:
@@ -118,6 +131,13 @@ We can generate histograms of the charges for each atom type using
     fix myhis1 grpSi ave/histo 10 500 5000 -1.5 2.5 1000 v_vq file relax-Si.histo mode vector
     fix myhis2 grpO ave/histo 10 500 5000 -1.5 2.5 1000 v_vq file relax-O.histo mode vector
 
+The ``fix ave/histo`` command samples values
+over a group of atoms and builds a histogram over a specified range divided into
+bins.  In this tutorial, it is used to monitor the charge distributions
+of silicon and oxygen atoms.  The parameters ``10 500 5000`` specify how often
+the histogram is updated and averaged, ``-1.5 2.5`` set the value range, 
+``1000`` is the number of bins, and ``v\_vq`` is the variable being histogrammed.
+
 We can also use the ``fix reaxff/species`` to evaluate what species are
 present within the simulation.  It will be useful later when the system is deformed,
 and bonds are broken:
@@ -140,6 +160,13 @@ density of the system:
 
     write_data relax.data
 
+.. admonition:: Note
+    :class: non-title-info
+
+    With the `aniso` keyword, the three dimensions of the simulation
+    box can change independently.  This is particularly relevant for solids and other
+    systems where anisotropic stresses may develop.
+
 Run the **relax.lmp** file using LAMMPS.  As seen from **relax.species**,
 only one species is detected, called ``O384Si192``, representing the entire system.
 
@@ -213,7 +240,7 @@ file, which must contain the following lines:
     read_data relax.data
 
     pair_style reaxff NULL safezone 3.0 mincap 150
-    pair_coeff * * reaxCHOFe.inc Si O
+    pair_coeff * * ffield.reax.CHOFe Si O
     fix myqeq all qeq/reaxff 1 0.0 10.0 1.0e-6 reaxff maxiter 400
 
     group grpSi type Si
@@ -243,7 +270,7 @@ Nosé-Hoover thermostat without a barostat:
     timestep 0.5
 
 Here, no barostat is used because the change in the box volume will be imposed
-by the ``fix deform``.
+by the ``fix deform``, see below.
 
 Let us run for 5000 steps without deformation, then apply the ``fix deform``
 to progressively elongate the box along the :math:`x`-axis during 25000 steps.  Add
@@ -259,6 +286,10 @@ the following line to **deform.lmp**:
 
     write_data deform.data
 
+The ``fix deform`` command applies a continuous deformation
+by elongating the simulation box along the x-axis at a constant engineering
+shear strain rate, specified by ``erate``, of :math:`5 \times 10^{-5}~\text{fs}^{-1}`.
+
 Run the **deform.lmp** file using LAMMPS.  During the deformation, the charge
 values progressively evolve until the structure eventually breaks down.  After the
 structure breaks down, the charges equilibrate near new average values that differ
@@ -345,7 +376,7 @@ Open the **decorate.lmp** file, which must contain the following lines:
     displace_atoms all move -12 0 0 # optional
 
     pair_style reaxff NULL safezone 3.0 mincap 150
-    pair_coeff * * reaxCHOFe.inc Si O H
+    pair_coeff * * ffield.reax.CHOFe Si O H
     fix myqeq all qeq/reaxff 1 0.0 10.0 1.0e-6 reaxff maxiter 400
 
 The ``displace_atoms`` command is used to move the center of the
diff --git a/docs/sphinx/source/tutorial6/introduction.rst b/docs/sphinx/source/tutorial6/introduction.rst
index c872ca88..4201aafb 100644
--- a/docs/sphinx/source/tutorial6/introduction.rst
+++ b/docs/sphinx/source/tutorial6/introduction.rst
@@ -15,6 +15,6 @@ grand canonical Monte Carlo simulations to compute the adsorption of water
 molecules in cracked silica material.  This tutorial
 illustrates the use of the grand canonical ensemble in molecular simulation, an
 open ensemble where the number of atoms or molecules in the simulation box can vary.
-By employing the grand canonical ensemble, we will set the chemical
-potential of water within a nanoporous :math:`\text{SiO}_2` structure.
+By employing the grand canonical ensemble, we simulate water in a nanoporous
+:math:`\text{SiO}_2` structure at a specified chemical potential.
 
diff --git a/docs/sphinx/source/tutorial6/tutorial.rst b/docs/sphinx/source/tutorial6/tutorial.rst
index 66a00fd9..74ddaa74 100644
--- a/docs/sphinx/source/tutorial6/tutorial.rst
+++ b/docs/sphinx/source/tutorial6/tutorial.rst
@@ -37,7 +37,7 @@ Add the following lines to **generate.lmp**:
     create_atoms O random 480 1072 box overlap 2.0 maxtry 500
 
 The ``create_atoms`` commands are used to place
-240 Si atoms, and 480 atoms, respectively.  This corresponds to
+240 Si atoms and 480 O atoms, respectively.  This corresponds to
 an initial density of approximately :math:`2 \, \text{g/cm}^3`, which is close
 to the expected final density of amorphous silica at 300 K.
 
@@ -122,7 +122,7 @@ generally suitable for a solid phase.
 
 Run the simulation using LAMMPS.  From the ``Charts`` window, the temperature
 evolution can be observed, showing that it closely follows the desired annealing procedure.
-The evolution of the box dimensions over time confirms that the box deformed during the
+The evolution of the box dimensions over time confirms that the box is deforming during the
 last stage of the simulation.  After the simulation completes, the final LAMMPS topology
 file called **generate.data** will be located next to **generate.lmp**.
 
@@ -184,12 +184,20 @@ the **cracking.lmp** file:
 
     write_data cracking.data
 
-The ``fix nvt`` command is employed to control the temperature of the system.
+The ``fix nvt`` command integrates the Nosé-Hoover equations
+of motion and is employed to control the temperature of the system.
 As observed from the generated images, the atoms
 progressively adjust to the changing box dimensions.  At some point,
 bonds begin to break, leading to the appearance of
 dislocations.
 
+.. admonition:: Note
+    :class: non-title-info
+
+    Although the Nosé-Hoover equations were originally formulated to sample the
+    NVT ensemble, using the ``fix nvt`` command does not guarantee that 
+    a simulation actually samples the NVT ensemble.
+
 .. figure:: figures/cracked-dark.png
     :class: only-dark
     :alt: Amorphous cracked silica block
@@ -215,15 +223,16 @@ random positions are made.  Each attempt is either accepted or rejected
 based on energy considerations.  For further details, please refer to
 classical textbooks like Ref. :cite:`frenkel2023understanding`.
 
-Using hydrid potentials
+Adapting the pair style
 -----------------------
 
-The first particularly of our system is that it combines water and
-silica, which necessitates the use of two force fields: Vashishta (for
-:math:`\text{SiO}_2`), and TIP4P (for water).  Here, the TIP4P/2005 model is
-employed for the water :cite:`abascal2005general`.
-
-Create a new file called **gcmc.lmp**, and copy the following lines into it:
+For this next step, we need to define the parameters for the water molecules and
+the cross-interactions between water and silica. The TIP4P/2005 model is employed
+for the water :cite:`abascal2005general`, while no specific parameters are set
+for the silica itself. The atoms of the silica will remain frozen during this part.
+Only the cross-interactions between water and silica need
+to be defined. Create a new file called **gcmc.lmp**, and copy the following
+lines into it:
 
 .. code-block:: lammps
 
@@ -232,7 +241,7 @@ Create a new file called **gcmc.lmp**, and copy the following lines into it:
     atom_style full
     neighbor 1.0 bin
     neigh_modify delay 1
-    pair_style hybrid/overlay vashishta lj/cut/tip4p/long OW HW OW-HW HW-OW-HW 0.1546 10
+    pair_style lj/cut/tip4p/long OW HW OW-HW HW-OW-HW 0.1546 10
     kspace_style pppm/tip4p 1.0e-5
     bond_style harmonic
     angle_style harmonic
@@ -242,14 +251,20 @@ Create a new file called **gcmc.lmp**, and copy the following lines into it:
 
     Open the **gcmc.lmp** file.
 
-Combining the two force fields, Vashishta and TIP4P/2005, is achieved
-using the ``hybrid/overlay`` pair style.  The PPPM
-solver :cite:`luty1996calculating` is specified with the ``kspace``
+The PPPM solver :cite:`luty1996calculating` is specified with the ``kspace``
 command, and is used to compute the long-range Coulomb interactions associated
 with ``tip4p/long``.  Finally, the style for the bonds
 and angles of the water molecules are defined; however, these specifications are
 not critical since TIP4P/2005 is a rigid water model.
 
+.. admonition:: Note
+    :class: non-title-info
+
+    In practice, it is possible to use both ``vashishta`` and
+    ``lj/cut/tip4p/long`` pair styles by employing the ``pair_style hybrid``
+    command.  However, hybridizing force fields should be done with caution, as there
+    is no guarantee that the resulting force field will produce meaningful results.
+
 The water molecule template called |H2O_mol_6|
 must be downloaded and located next to **gcmc.lmp**.
 
@@ -331,26 +346,31 @@ to **gcmc.lmp**:
 
 .. code-block:: lammps
         
-    pair_coeff * * vashishta SiO.1990.vashishta Si O NULL NULL
-    pair_coeff * * lj/cut/tip4p/long 0 0
-    pair_coeff Si OW lj/cut/tip4p/long 0.0057 4.42
-    pair_coeff O OW lj/cut/tip4p/long 0.0043 3.12
-    pair_coeff OW OW lj/cut/tip4p/long 0.008 3.1589
-    pair_coeff HW HW lj/cut/tip4p/long 0.0 0.0
+    pair_coeff * * 0 0
+    pair_coeff Si OW 0.0057 4.42
+    pair_coeff O OW 0.0043 3.12
+    pair_coeff OW OW 0.008 3.1589
+    pair_coeff HW HW 0.0 0.0
     bond_coeff OW-HW 0 0.9572
     angle_coeff HW-OW-HW 0 104.52
 
-The force field Vashishta applies only to ``Si`` and ``O`` of :math:`\text{SiO}_2`,
-and not to the ``OW`` and ``HW`` of :math:`\text{H}_2\text{O}`, thanks to the ``NULL`` parameters
-used for atoms of types ``OW`` and ``HW``.  Pair coefficients for the ``lj/cut/tip4p/long``
+Pair coefficients for the ``lj/cut/tip4p/long``
 potential are defined between O(:math:`\text{H}_2\text{O}`) and between H(:math:`\text{H}_2\text{O}`)
 atoms, as well as between O(:math:`\text{SiO}_2`)-O(:math:`\text{H}_2\text{O}`) and
-Si(:math:`\text{SiO}_2`)-O(:math:`\text{H}_2\text{O}`). Thus,  the fluid-fluid and the
+Si(:math:`\text{SiO}_2`)-O(:math:`\text{H}_2\text{O}`).  Thus, the fluid-fluid and the
 fluid-solid interactions will be adressed with by the ``lj/cut/tip4p/long`` potential.
 The ``bond_coeff`` and ``angle_coeff`` commands set the ``OW-HW``
 bond length to 0.9572 Å, and the ``HW-OW-HW``
 angle to :math:`104.52^\circ`, respectively :cite:`abascal2005general`.
 
+.. admonition:: Note
+    :class: non-title-info
+
+    The pair coefficients for interactions between Si(:math:`\text{SiO}_2`)
+    and O(:math:`\text{SiO}_2`) are set by the first command, ``pair_coeff * * 0 0``,
+    which effectively means that they do not interact. This is acceptable here because
+    the silica atoms remain frozen during this part of the tutorial.
+
 Add the following lines to **gcmc.lmp** as well:
 
 .. code-block:: lammps
@@ -361,6 +381,14 @@ Add the following lines to **gcmc.lmp** as well:
 
     fix shak H2O shake 1.0e-5 200 0 b OW-HW a HW-OW-HW mol h2omol
 
+.. admonition:: Note
+    :class: non-title-info
+
+    Here, a variable of type *atom* is used.  Such variable 
+    defines a per-atom property, i.e., it evaluates the specified expression 
+    separately for each atom.  This is often used to select atoms based on 
+    their properties or types.
+
 The number of oxygen atoms from water molecules (i.e. the number of molecules)
 is calculated by the ``nO`` variable.  The SHAKE algorithm is used to
 maintain the shape of the water molecules over time :cite:`ryckaert1977numerical, andersen1983rattle`.
@@ -387,19 +415,16 @@ following lines into **gcmc.lmp**:
 .. code-block:: lammps
 
     compute ctH2O H2O temp
-    compute_modify thermo_temp dynamic yes
-    compute_modify ctH2O dynamic yes
-    fix mynvt1 H2O nvt temp 300 300 0.1
-    fix_modify mynvt1 temp ctH2O
-    fix mynvt2 SiO nvt temp 300 300 0.1
+    compute_modify thermo_temp dynamic/dof yes
+    compute_modify ctH2O dynamic/dof yes
+    fix mynvt H2O nvt temp 300 300 0.1
+    fix_modify mynvt temp ctH2O
     timestep 0.001
 
-Two different thermostats are used for :math:`\text{SiO}_2` and :math:`\text{H}_2\text{O}`,
-respectively.  Using separate thermostats is usually better when the system contains
-two separate species, such as a solid and a liquid.  It is particularly important
-to use two thermostats here because the number of water molecules will fluctuate
-with time.  The ``compute_modify`` command with the ``dynamic yes``
-option for water is used to specify that the number of molecules will not be constant.
+Here, the ``fix nvt`` applies only to the water molecules, so
+the atoms in the silica remain fixed.  The ``compute_modify`` command with
+the ``dynamic/dof yes`` option is used for water to account for the fact
+that the number of molecules is not constant.
 
 Finally, let us use the ``fix gcmc`` and perform the grand canonical Monte
 Carlo steps.  Add the following lines into **gcmc.lmp**:
@@ -409,9 +434,15 @@ Carlo steps.  Add the following lines into **gcmc.lmp**:
     variable tfac equal 5.0/3.0
     fix fgcmc H2O gcmc 100 100 0 0 65899 300 -0.5 0.1 mol h2omol tfac_insert ${tfac} shake shak full_energy pressure 100
 
+The ``fix gcmc`` command performs grand canonical Monte Carlo
+moves to insert, delete, or swap molecules. Here, 100 attempts are made every
+100 steps.  The ``mol h2omol`` keyword specifies the
+molecule type being inserted/deleted, while ``shake shak`` enforces rigid
+molecular constraints during these moves. With the ``pressure 100`` keyword,
+a fictitious reservoir with a pressure of 100 atmospheres is used.
 The ``tfac_insert`` option ensures the correct estimate for the temperature
 of the inserted water molecules by taking into account the internal degrees of
-freedom.  Here, 100 insertion and deletion attemps are made every 100 steps.
+freedom.
 
 .. admonition:: Note
     :class: non-title-info
@@ -430,12 +461,17 @@ Finally, let us print some information and run for 25 ps:
 
     run 25000
 
-Running this simulation using LAMMPS, one can see that the number of molecules is increasing
-progressively.  When using the pressure argument, LAMMPS ignores the value of the
-chemical potential (here :math:`\mu = -0.5\,\text{eV}`, which corresponds roughly to
-ambient conditions, i.e. to a relative humidity :math:`\text{RH} \approx 50\,\%` :cite:`gravelle2020multi`.)
-The large pressure value of 100 bars was chosen to ensure that some successful
-insertions of molecules would occur during the short duration of this simulation.
+The ``f_`` keywords extract the Monte Carlo move statistics output by the
+``fix gcmc`` command.
+
+.. admonition:: Note
+    :class: non-title-info
+        
+    When using the pressure argument, LAMMPS ignores the value of the
+    chemical potential (here :math:`\mu = -0.5\,\text{eV}`, which corresponds roughly to
+    ambient conditions, i.e. to a relative humidity :math:`\text{RH} \approx 50\,\%` :cite:`gravelle2020multi`.)
+    The large pressure value of 100 bars was chosen to ensure that some successful
+    insertions of molecules would occur during the short duration of this simulation.
 
 .. figure:: figures/GCMC-number-dm.png
     :class: only-dark
@@ -449,7 +485,8 @@ insertions of molecules would occur during the short duration of this simulation
 
     Figure: Number of water molecules, :math:`N_\text{H2O}`, as a function of time, :math:`t`.
 
-After a few GCMC steps, the number of molecules starts increasing.  Once the
+Running this simulation using LAMMPS, one can see that
+after a few GCMC steps, the number of molecules starts increasing.  Once the
 crack is filled with water molecules, the total number of molecules reaches a plateau.  The final number of
 molecules depends on the imposed pressure, temperature, and the interaction
 between water and silica (i.e. its hydrophilicity).  Note that GCMC simulations
diff --git a/docs/sphinx/source/tutorial7/tutorial.rst b/docs/sphinx/source/tutorial7/tutorial.rst
index 1fc334c2..68e73b2c 100644
--- a/docs/sphinx/source/tutorial7/tutorial.rst
+++ b/docs/sphinx/source/tutorial7/tutorial.rst
@@ -11,17 +11,18 @@
 Method 1: Free sampling
 =======================
 
-The most direct way to calculate a free energy profile is to extract the
-partition function from a classical (i.e. unbiased) molecular dynamics
-simulation, and then estimate the Gibbs free energy by using
+The most direct way to estimate a free energy profile is to sample
+the Boltzmann distribution using a classical (i.e.unbiased) molecular dynamics
+simulation, and compute relative Gibbs free energies from the relative probabilities
+of states using
 
 .. math::
     :label: eq_G
 
-    \Delta G = -RT \ln(p/p_0),
+    \Delta G = -RT \ln(\rho/\rho_0),
 
 where :math:`\Delta G` is the free energy difference, :math:`R` is the gas constant, :math:`T`
-is the temperature, :math:`p` is the pressure, and :math:`p_0` is a reference pressure.
+is the temperature, :math:`\rho` is the density, and :math:`\rho_0` is a reference density.
 As an illustration, let us apply this method to a simple configuration
 that consists of a particles in a box in the presence of a
 position-dependent repulsive force that makes the center of the box a less
@@ -48,7 +49,7 @@ content:
 
     units real
     atom_style atomic
-    pair_style lj/cut 3.822
+    pair_style lj/cut $(2^(1/6)*v_sigma)
     pair_modify shift yes
     boundary p p p
 
@@ -56,12 +57,18 @@ Here, we begin by defining variables for the Lennard-Jones interaction
 :math:`\sigma` and :math:`\epsilon` and for the repulsive potential
 :math:`U`, which are :math:`U_0`, :math:`\delta`, and
 :math:`x_0` [see Eqs. :eq:`eq_U`-:eq:`eq_F` below].  The cut-off value of
-3.822 Å was chosen to create a Weeks-Chandler-Andersen (WCA) potential,
-which is a truncated and purely repulsive LJ
-potential :cite:`weeks1971role`.  It was calculated as :math:`2^{1/6} \sigma`.
+:\math:`2^{1/6} \sigma = 3.822` was chosen to create a Weeks-Chandler-Andersen
+(WCA) potential, which is a truncated and purely repulsive LJ potential :cite:`weeks1971role`. 
 The potential is also shifted to be equal to 0 at the cut-off
 using the ``pair_modify`` command.
 
+.. admonition:: Note
+    :class: non-title-info
+
+    The syntax ``$(...)``, where a dollar sign is followed by parentheses, allows
+    you to evaluate a numeric formula immediately, without having to assign it
+    to a named variable first.
+
 System creation and settings
 ----------------------------
 
@@ -77,6 +84,12 @@ following lines to **free-sampling.lmp**:
     mass * 39.95
     pair_coeff * * ${epsilon} ${sigma}
 
+.. admonition:: Note
+    :class: non-title-info
+
+    In the ``pair_coeff`` command, the first two asterisks
+    ``* *`` indicate that the parameters apply to all atom types in the simulation.
+
 The variables :math:`U_0`, :math:`\delta`, and :math:`x_0`, defined in the previous subsection, are
 used here to create the repulsive potential, restricting the atoms from exploring
 the center of the box:
@@ -208,6 +221,10 @@ Add the following line to **free-sampling.lmp**:
 
     run 2000000
 
+Here, the ``chunk/atom`` command discretizes the simulation
+domain into spatial bins of size 2~\AA{} along the :math:`x` direction,
+and the ``ave/chunk`` command computes and outputs the number density of
+atoms within each bin to the file **free-sampling.dat**.}
 The step count is reset to 0 using ``reset_timestep`` to synchronize it
 with the output times of ``fix density/number``.  Run the simulation using
 LAMMPS.
@@ -232,9 +249,8 @@ Data analysis
 Once the simulation is complete, the density profile from **free-sampling.dat**
 shows that the density in the center of the box is
 about two orders of magnitude lower than inside the reservoir.
-Next, we plot :math:`-R T \ln(\rho/\rho_\mathrm{bulk})` (i.e. Eq. :eq:`eq_G` where
-the pressure ratio :math:`p/p_\mathrm{bulk}` is replaced by the density ratio
-:math:`\rho/\rho_\mathrm{bulk}`, assuming the system behaves as an ideal gas) and compare it
+Next, we plot :math:`-R T \ln(\rho/\rho_\mathrm{bulk})`, where :math:`\rho/\rho_\mathrm{bulk}`
+is the the density ratio,  and compare it
 with the imposed potential :math:`U` from Eq. :eq:`eq_U`.
 The reference density, :math:`\rho_\text{bulk} = 0.0009~\text{Å}^{-3}`,
 was estimated by measuring the density of the reservoir from the raw density
@@ -303,7 +319,7 @@ and paste in the following content:
 
     units real
     atom_style atomic
-    pair_style lj/cut 3.822
+    pair_style lj/cut $(2^(1/6)*v_sigma)
     pair_modify shift yes
     boundary p p p
 
diff --git a/docs/sphinx/source/tutorial8/introduction.rst b/docs/sphinx/source/tutorial8/introduction.rst
index cd7b9d82..1880e5ce 100644
--- a/docs/sphinx/source/tutorial8/introduction.rst
+++ b/docs/sphinx/source/tutorial8/introduction.rst
@@ -16,4 +16,6 @@ protocol is used to simulate the polymerization of styrene monomers, and the
 polymerization reaction is tracked over time :cite:`gissinger2017polymer,
 gissinger2020reacter, gissinger2024molecular`. In contrast to AIREBO
 :ref:`carbon-nanotube-label` and ReaxFF :ref:`reactive-silicon-dioxide-label`,
-the REACTER protocol relies on the use of a *classical* force field.
\ No newline at end of file
+the REACTER protocol relies on the use of a *classical* force field
+that does not inherently model bond formation or breaking, but instead couples
+with an external algorithm to simulate polymerization reactions.
\ No newline at end of file
diff --git a/docs/sphinx/source/tutorial8/tutorial.rst b/docs/sphinx/source/tutorial8/tutorial.rst
index 569bd746..3a806614 100644
--- a/docs/sphinx/source/tutorial8/tutorial.rst
+++ b/docs/sphinx/source/tutorial8/tutorial.rst
@@ -24,6 +24,8 @@ following content corresponding to **mixing.lmp**:
 The ``class2`` styles compute a 6/9 Lennard-Jones potential :cite:`sun1998compass`.
 The ``class2`` bond, angle, dihedral, and improper styles are used as
 well, see the documentation for a description of their respective potentials.
+The ``tail yes`` option adds long-range van der Waals tail corrections to the
+energy and pressure.
 The ``mix sixthpower`` imposes the following mixing rule for the calculation
 of the cross coefficients:
 
@@ -213,6 +215,23 @@ for polymer.
 
     <a href="https://raw.githubusercontent.com/lammpstutorials/lammpstutorials-inputs/refs/heads/main/tutorial8/P-P.rxnmap" target="_blank">P-P.rxnmap</a>
 
+.. admonition:: Note
+    :class: non-title-info
+        
+    The data stored in molecule templates include atom coordinates,
+    partial charges, molecule IDs, atom types, and interaction types for bonds,
+    angles, dihedrals and impropers.  The map files contain information about
+    the reaction.  The first mandatory section of the map files begins with the
+    keyword “InitiatorIDs” and lists the two atom IDs of the initiator atom pair
+    in the pre-reacted molecule template.  The second mandatory section begins
+    with the keyword “Equivalences” and lists a one-to-one correspondence between
+    atom IDs of the pre- and post-reacted templates.  Some atoms in the pre-reacted
+    template that are not reacting may have missing topology with respect to the
+    simulation.  For example, the pre-reacted template may contain an atom that,
+    in the simulation, is currently connected to the rest of a long polymer
+    chain.  These are referred to as edge atoms, and are also specified in the
+    map file in the “EdgeIDs” section.
+
 Simulating the reaction
 -----------------------
 
@@ -277,7 +296,14 @@ each reaction is stabilized for 60 time steps.  Each ``react`` keyword
 corresponds to a reaction, e.g., a transformation of ``mol1`` into ``mol2``
 based on the atom map **M-M.rxnmap**.  Implementation details about each reaction,
 such as the reaction distance cutoffs and the frequency with which to search for
-reaction sties, are also specified in this command.
+reaction sites, are also specified in this command.
+
+.. admonition:: Note
+    :class: non-title-info
+
+    The ``fix nve/limit`` command integrates Newton's equations of motion
+    while limiting the maximum displacement of atoms per timestep.  This is
+    useful for preventing atoms from moving too far due to large forces.
 
 .. figure:: figures/REACT-composite-dm.png
     :class: only-dark