From 5f5bfc7ab97c8027bb60eedd65cdcd66b5556b57 Mon Sep 17 00:00:00 2001 From: Pablo Zubieta <8410335+pabloferz@users.noreply.github.com> Date: Thu, 31 Oct 2024 12:03:46 -0500 Subject: [PATCH] Improve wording across example Colab notebooks (#343) Also fixes some small bugs introduced in #342 in one of the notebooks. --- examples/Advanced_Sampling_Introduction.ipynb | 28 +++++++++---------- examples/Advanced_Sampling_Introduction.md | 28 +++++++++---------- .../Installing_a_PySAGES_Environment.ipynb | 4 +-- examples/Installing_a_PySAGES_Environment.md | 4 +-- examples/hoomd-blue/ann/Butane_ANN.ipynb | 20 ++++++------- examples/hoomd-blue/ann/Butane_ANN.md | 20 ++++++------- examples/hoomd-blue/cff/Butane_CFF.ipynb | 14 +++++----- examples/hoomd-blue/cff/Butane_CFF.md | 14 +++++----- examples/hoomd-blue/funn/Butane_FUNN.ipynb | 14 +++++----- examples/hoomd-blue/funn/Butane_FUNN.md | 14 +++++----- .../harmonic_bias/Harmonic_Bias.ipynb | 6 ++-- .../hoomd-blue/harmonic_bias/Harmonic_Bias.md | 6 ++-- .../spectral_abf/Butane-SpectralABF.ipynb | 14 +++++----- .../spectral_abf/Butane-SpectralABF.md | 14 +++++----- .../Umbrella_Integration.ipynb | 6 ++-- .../Umbrella_Integration.md | 6 ++-- .../openmm/harmonic_bias/Harmonic_Bias.ipynb | 12 ++++---- .../openmm/harmonic_bias/Harmonic_Bias.md | 12 ++++---- examples/openmm/metad/Metadynamics-ADP.ipynb | 10 +++---- examples/openmm/metad/Metadynamics-ADP.md | 10 +++---- .../openmm/metad/nacl/Metadynamics_NaCl.ipynb | 10 +++---- .../openmm/metad/nacl/Metadynamics_NaCl.md | 10 +++---- .../openmm/spectral_abf/ADP_SpectralABF.ipynb | 10 +++---- .../openmm/spectral_abf/ADP_SpectralABF.md | 10 +++---- 24 files changed, 143 insertions(+), 153 deletions(-) diff --git a/examples/Advanced_Sampling_Introduction.ipynb b/examples/Advanced_Sampling_Introduction.ipynb index b9a488d3..5bca0e45 100644 --- a/examples/Advanced_Sampling_Introduction.ipynb +++ b/examples/Advanced_Sampling_Introduction.ipynb @@ -25,8 +25,8 @@ "\n", "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance.\n" + "First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance.\n" ] }, { @@ -130,7 +130,7 @@ "\n", "## PySAGES\n", "\n", - "The next step is to install PySAGES. First, we need to install JAX. Fortunately, Colab already ships with JAX pre-installed (to learn how to install it you can look at the [JAX documentation](https://jax.readthedocs.io) for more details). To install PySAGES, we retrieve the latest version from GitHub and add its dependecies via `pip`.\n" + "The next step is to install PySAGES. First, we need to install JAX. Fortunately, Colab already ships with JAX pre-installed (to learn how to install it you can look at the [JAX documentation](https://jax.readthedocs.io) for more details). To install PySAGES, we retrieve the latest version from GitHub and add its dependencies via `pip`.\n" ] }, { @@ -183,7 +183,7 @@ "\n", "- Protein folding\n", "- Protein-ligand binding\n", - "- Liqid membrane fusion" + "- Liquid membrane fusion" ] }, { @@ -285,7 +285,7 @@ "\n", "Let's examine such a system via computer simulations.\n", "\n", - "- fast to integration\n", + "- fast to integrate\n", "- custom bond potential to shape the free-energy landscape\n", "\n", "$$P(r) = Ar^2 + A(1-e^{-r^2})\\cos(r p \\pi)$$\n" @@ -1073,7 +1073,7 @@ "\n", "We want to find the free-energy profile along a given path in the space for collective variables. Usually, this path can be multidimensional.\n", "\n", - "Example dihedral angles of Alanine Dipeptide. [PySAGES Alanine Dipentide examples](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/Harmonic_Bias.ipynb)\n", + "Example dihedral angles of Alanine Dipeptide. [PySAGES Alanine Dipeptide examples](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/Harmonic_Bias.ipynb)\n", "\n", "\n", "\n", @@ -1459,7 +1459,7 @@ "General Idea:\n", "\n", "Move the string (path) along the perpendicular component $\\Delta V^\\perp$ at each discretized point $v_i = - \\Delta V^\\perp$. (Gradient descent)\n", - "The parallel component would move the discretized point just along the path and end up in the minima.\n", + "The parallel component would move the discretized point just along the path and end up in a minimum.\n", "\n", "Problem 1:\n", "\n", @@ -1489,7 +1489,7 @@ "\n", "### Improved string method\n", "\n", - "Using the perpendicular component directly does not lead to good convergences.\n", + "Using the perpendicular component directly does not lead to good convergence.\n", "Instead, we can use the improved string method.\n", "\n", "Iterate:\n", @@ -1497,12 +1497,10 @@ "1. Calculate $\\Delta V|_{t_i}$\n", "2. Move string $\\xi_0(t_i)$ += $\\alpha \\Delta V|_{t_i}$\n", "3. Calculate spline $\\xi_0^s(t)$\n", - "4. Re-parametrize the string points.\n", - " > Such that $|\\xi*0^s(t_i) - \\xi_0^s(t*{i+1})| = $const.\n", - " >\n", - " > - cancels out parallel force component\n", - " > - well-discretized string\n", - " > - requires a norm in $\\xi$ space. For example $L^2$ norm in $\\xi$ space.\n", + "4. Re-parametrize the string points such that $|\\xi_0^s(t_i) - \\xi_0^s(t_{i+1})| = const$.\n", + " - cancels out parallel force component\n", + " - well-discretized string\n", + " - requires a norm in $\\xi$ space. For example $L^2$ norm in $\\xi$ space.\n", "\n", "Stop iteration if converged and test $\\Delta V^\\perp = 0$.\n", "\n", @@ -1514,7 +1512,7 @@ "\n", "Let us define a committor probability.\n", "\n", - "For a given point $r_i$ we can define the probability that a simulation started from this point ends in minima $B$ first before it moves through $B$.\n", + "For a given point $r_i$ we can define the probability that a simulation started from this point ends in minimum $B$ first before it moves through $B$.\n", "$$\\text{commitor probability B: } p_B(r_i)$$\n", "\n", "This is a probability since we can have multiple realizations of $r_i$ in momentum space (Maxwell-Boltzmann distribution). Each of these realizations has its path and we simulate them and measure if they arrive in $A$ or $B$ first." diff --git a/examples/Advanced_Sampling_Introduction.md b/examples/Advanced_Sampling_Introduction.md index a9ea1798..f7f056f6 100644 --- a/examples/Advanced_Sampling_Introduction.md +++ b/examples/Advanced_Sampling_Introduction.md @@ -29,8 +29,8 @@ The University of Chicago # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. @@ -76,7 +76,7 @@ We'll also need some additional python dependencies ## PySAGES -The next step is to install PySAGES. First, we need to install JAX. Fortunately, Colab already ships with JAX pre-installed (to learn how to install it you can look at the [JAX documentation](https://jax.readthedocs.io) for more details). To install PySAGES, we retrieve the latest version from GitHub and add its dependecies via `pip`. +The next step is to install PySAGES. First, we need to install JAX. Fortunately, Colab already ships with JAX pre-installed (to learn how to install it you can look at the [JAX documentation](https://jax.readthedocs.io) for more details). To install PySAGES, we retrieve the latest version from GitHub and add its dependencies via `pip`. @@ -106,7 +106,7 @@ Many systems have rugged free-energy landscapes, where different basins are divi - Protein folding - Protein-ligand binding -- Liqid membrane fusion +- Liquid membrane fusion @@ -189,7 +189,7 @@ $$p \propto e^{-\beta \Delta E} \ll 1$$ Let's examine such a system via computer simulations. -- fast to integration +- fast to integrate - custom bond potential to shape the free-energy landscape $$P(r) = Ar^2 + A(1-e^{-r^2})\cos(r p \pi)$$ @@ -645,7 +645,7 @@ Can we bias simulations in these regions too, to improve sampling coverage? We want to find the free-energy profile along a given path in the space for collective variables. Usually, this path can be multidimensional. -Example dihedral angles of Alanine Dipeptide. [PySAGES Alanine Dipentide examples](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/Harmonic_Bias.ipynb) +Example dihedral angles of Alanine Dipeptide. [PySAGES Alanine Dipeptide examples](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/Harmonic_Bias.ipynb) @@ -916,7 +916,7 @@ The normal component of the force $\Rightarrow \Delta V^\perp = 0$ is zero. General Idea: Move the string (path) along the perpendicular component $\Delta V^\perp$ at each discretized point $v_i = - \Delta V^\perp$. (Gradient descent) -The parallel component would move the discretized point just along the path and end up in the minima. +The parallel component would move the discretized point just along the path and end up in a minimum. Problem 1: @@ -946,7 +946,7 @@ $\Rightarrow$ smooth path $\xi_0^s(t)$ with $\xi_0^s(t) = \xi(t_i)$. ### Improved string method -Using the perpendicular component directly does not lead to good convergences. +Using the perpendicular component directly does not lead to good convergence. Instead, we can use the improved string method. Iterate: @@ -954,12 +954,10 @@ Iterate: 1. Calculate $\Delta V|_{t_i}$ 2. Move string $\xi_0(t_i)$ += $\alpha \Delta V|_{t_i}$ 3. Calculate spline $\xi_0^s(t)$ -4. Re-parametrize the string points. - > Such that $|\xi*0^s(t_i) - \xi_0^s(t*{i+1})| = $const. - > - > - cancels out parallel force component - > - well-discretized string - > - requires a norm in $\xi$ space. For example $L^2$ norm in $\xi$ space. +4. Re-parametrize the string points such that $|\xi_0^s(t_i) - \xi_0^s(t_{i+1})| = const$. + - cancels out parallel force component + - well-discretized string + - requires a norm in $\xi$ space. For example $L^2$ norm in $\xi$ space. Stop iteration if converged and test $\Delta V^\perp = 0$. @@ -971,7 +969,7 @@ With the converged path, calculate the free-energy profile using umbrella integr Let us define a committor probability. -For a given point $r_i$ we can define the probability that a simulation started from this point ends in minima $B$ first before it moves through $B$. +For a given point $r_i$ we can define the probability that a simulation started from this point ends in minimum $B$ first before it moves through $B$. $$\text{commitor probability B: } p_B(r_i)$$ This is a probability since we can have multiple realizations of $r_i$ in momentum space (Maxwell-Boltzmann distribution). Each of these realizations has its path and we simulate them and measure if they arrive in $A$ or $B$ first. diff --git a/examples/Installing_a_PySAGES_Environment.ipynb b/examples/Installing_a_PySAGES_Environment.ipynb index 091f587e..32a2e8b2 100644 --- a/examples/Installing_a_PySAGES_Environment.ipynb +++ b/examples/Installing_a_PySAGES_Environment.ipynb @@ -243,7 +243,7 @@ "\n", "## Building and Installing the HOOMD-dlext Plugin\n", "\n", - "Now we can install the `dlext` plugin for HOOMD-blue. But, we need to get some dependecies first.\n" + "Now we can install the `dlext` plugin for HOOMD-blue. But, we need to get some dependencies first.\n" ] }, { @@ -320,7 +320,7 @@ "\n", "# Adding OpenMM Support\n", "\n", - "Having previously set our the environment, we can now just simply install some required dependencies and build and install OpenMM.\n", + "Having previously set up the environment, we can now just simply install some required dependencies and build and install OpenMM.\n", "\n", "Again, installing dependencies will be different depending on your operating system and python environment." ] diff --git a/examples/Installing_a_PySAGES_Environment.md b/examples/Installing_a_PySAGES_Environment.md index 8b12285c..419707b1 100644 --- a/examples/Installing_a_PySAGES_Environment.md +++ b/examples/Installing_a_PySAGES_Environment.md @@ -148,7 +148,7 @@ cmake --build $BUILD_PATH --target install -j8 > /dev/null ## Building and Installing the HOOMD-dlext Plugin -Now we can install the `dlext` plugin for HOOMD-blue. But, we need to get some dependecies first. +Now we can install the `dlext` plugin for HOOMD-blue. But, we need to get some dependencies first. @@ -191,7 +191,7 @@ import hoomd.dlext # Adding OpenMM Support -Having previously set our the environment, we can now just simply install some required dependencies and build and install OpenMM. +Having previously set up the environment, we can now just simply install some required dependencies and build and install OpenMM. Again, installing dependencies will be different depending on your operating system and python environment. diff --git a/examples/hoomd-blue/ann/Butane_ANN.ipynb b/examples/hoomd-blue/ann/Butane_ANN.ipynb index 4ca6c997..8e73876a 100644 --- a/examples/hoomd-blue/ann/Butane_ANN.ipynb +++ b/examples/hoomd-blue/ann/Butane_ANN.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -112,7 +112,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { @@ -157,7 +157,7 @@ "source": [ "ANN gradually learns the free energy from a probability density estimate based on the frequency of visits to the grid on collective variable space.\n", "\n", - "For this Colab, we are using butane as the example molecule." + "In this Colab notebook, we will use butane as an example system." ] }, { @@ -435,7 +435,7 @@ }, "source": [ "\n", - "Next, we load PySAGES and the relevant classes and methods for our problem\n" + "Next, we import PySAGES and the necessary classes and methods for our simulation\n" ] }, { @@ -494,8 +494,8 @@ }, "source": [ "\n", - "We now simulate $5\\times10^5$ time steps.\n", - "Make sure to run with GPU support, otherwise, it can take a very long time.\n" + "We will now run a simulation for $5\\times10^5$ time steps.\n", + "For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete.\n" ] }, { @@ -688,8 +688,7 @@ "id": "PXBKUfK0p9T2" }, "source": [ - "\n", - "Let's now plot the free energy landscape learned by the ANN sampling method.\n" + "We can now visualize the free energy landscape learned by the ANN sampling method." ] }, { @@ -768,8 +767,7 @@ "id": "Kf_CMdih90Cd" }, "source": [ - "\n", - "You can compare this with the free energy landscape for the different conformations of butane.\n" + "You can compare this with the free energy landscape for the different conformations of butane." ] } ], diff --git a/examples/hoomd-blue/ann/Butane_ANN.md b/examples/hoomd-blue/ann/Butane_ANN.md index 1a25055f..11a703f1 100644 --- a/examples/hoomd-blue/ann/Butane_ANN.md +++ b/examples/hoomd-blue/ann/Butane_ANN.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -61,7 +61,7 @@ We'll also need some additional python dependencies ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" @@ -81,7 +81,7 @@ cd /content/ann ANN gradually learns the free energy from a probability density estimate based on the frequency of visits to the grid on collective variable space. -For this Colab, we are using butane as the example molecule. +In this Colab notebook, we will use butane as an example system. ```python id="BBvC7Spoog82" @@ -347,7 +347,7 @@ def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), s -Next, we load PySAGES and the relevant classes and methods for our problem +Next, we import PySAGES and the necessary classes and methods for our simulation @@ -384,8 +384,8 @@ method = ANN(cvs, grid, topology, kT) -We now simulate $5\times10^5$ time steps. -Make sure to run with GPU support, otherwise, it can take a very long time. +We will now run a simulation for $5\times10^5$ time steps. +For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete. @@ -394,9 +394,7 @@ run_result = pysages.run(method, generate_simulation, int(5e5)) ``` - -Let's now plot the free energy landscape learned by the ANN sampling method. - +We can now visualize the free energy landscape learned by the ANN sampling method. ```python id="X69d1R7OpW4P" @@ -421,7 +419,5 @@ fig.show() ``` - You can compare this with the free energy landscape for the different conformations of butane. - diff --git a/examples/hoomd-blue/cff/Butane_CFF.ipynb b/examples/hoomd-blue/cff/Butane_CFF.ipynb index 049fe1aa..1ac5a643 100644 --- a/examples/hoomd-blue/cff/Butane_CFF.ipynb +++ b/examples/hoomd-blue/cff/Butane_CFF.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -112,7 +112,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { @@ -157,7 +157,7 @@ "source": [ "CFF gradually learns both the free energy and its gradient from a discrete estimate of the generalized mean forces (based on the same algorithm as the ABF method), and frequency of visits to sites in phase space. It employs a couple of neural networks to provide a continuous approximation to the free energy.\n", "\n", - "For this Colab, we are using butane as the example molecule." + "In this Colab notebook, we will use butane as an example system." ] }, { @@ -435,7 +435,7 @@ }, "source": [ "\n", - "Next, we load PySAGES and the relevant classes and methods for our problem\n" + "Next, we import PySAGES and the necessary classes and methods for our simulation\n" ] }, { @@ -493,8 +493,8 @@ }, "source": [ "\n", - "We now simulate $5\\times10^5$ time steps.\n", - "Make sure to run with GPU support, otherwise, it can take a very long time.\n" + "We will now run a simulation for $5\\times10^5$ time steps.\n", + "For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete.\n" ] }, { diff --git a/examples/hoomd-blue/cff/Butane_CFF.md b/examples/hoomd-blue/cff/Butane_CFF.md index 5016bee3..aa247399 100644 --- a/examples/hoomd-blue/cff/Butane_CFF.md +++ b/examples/hoomd-blue/cff/Butane_CFF.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -61,7 +61,7 @@ We'll also need some additional python dependencies ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" @@ -81,7 +81,7 @@ cd /content/cff CFF gradually learns both the free energy and its gradient from a discrete estimate of the generalized mean forces (based on the same algorithm as the ABF method), and frequency of visits to sites in phase space. It employs a couple of neural networks to provide a continuous approximation to the free energy. -For this Colab, we are using butane as the example molecule. +In this Colab notebook, we will use butane as an example system. ```python id="BBvC7Spoog82" @@ -347,7 +347,7 @@ def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), s -Next, we load PySAGES and the relevant classes and methods for our problem +Next, we import PySAGES and the necessary classes and methods for our simulation @@ -383,8 +383,8 @@ method = CFF(cvs, grid, topology, kT) -We now simulate $5\times10^5$ time steps. -Make sure to run with GPU support, otherwise, it can take a very long time. +We will now run a simulation for $5\times10^5$ time steps. +For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete. diff --git a/examples/hoomd-blue/funn/Butane_FUNN.ipynb b/examples/hoomd-blue/funn/Butane_FUNN.ipynb index ccbeb77c..7f520662 100644 --- a/examples/hoomd-blue/funn/Butane_FUNN.ipynb +++ b/examples/hoomd-blue/funn/Butane_FUNN.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -120,7 +120,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { @@ -173,7 +173,7 @@ "source": [ "FUNN gradually learns the free energy gradient from a discrete estimate based on the same algorithm as the ABF method, but employs a neural network to provide a continuous approximation to it.\n", "\n", - "For this Colab, we are using butane as the example molecule." + "In this Colab notebook, we will use butane as an example system." ] }, { @@ -450,7 +450,7 @@ "id": "3UrzENm_oo6U" }, "source": [ - "Next, we load PySAGES and the relevant classes and methods for our problem" + "Next, we import PySAGES and the necessary classes and methods for our simulation" ] }, { @@ -508,8 +508,8 @@ }, "source": [ "\n", - "We now simulate $5\\times10^5$ time steps.\n", - "Make sure to run with GPU support, otherwise, it can take a very long time.\n" + "We will now run a simulation for $5\\times10^5$ time steps.\n", + "For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete.\n" ] }, { diff --git a/examples/hoomd-blue/funn/Butane_FUNN.md b/examples/hoomd-blue/funn/Butane_FUNN.md index 03a5caad..0d923a1d 100644 --- a/examples/hoomd-blue/funn/Butane_FUNN.md +++ b/examples/hoomd-blue/funn/Butane_FUNN.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -61,7 +61,7 @@ We'll also need some additional python dependencies ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" @@ -81,7 +81,7 @@ cd /content/funn FUNN gradually learns the free energy gradient from a discrete estimate based on the same algorithm as the ABF method, but employs a neural network to provide a continuous approximation to it. -For this Colab, we are using butane as the example molecule. +In this Colab notebook, we will use butane as an example system. ```python id="BBvC7Spoog82" @@ -346,7 +346,7 @@ def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), s ``` -Next, we load PySAGES and the relevant classes and methods for our problem +Next, we import PySAGES and the necessary classes and methods for our simulation ```python id="fpMg-o8WomAA" @@ -381,8 +381,8 @@ method = FUNN(cvs, grid, topology) -We now simulate $5\times10^5$ time steps. -Make sure to run with GPU support, otherwise, it can take a very long time. +We will now run a simulation for $5\times10^5$ time steps. +For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete. diff --git a/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.ipynb b/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.ipynb index c191b26d..2809c71c 100644 --- a/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.ipynb +++ b/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -113,7 +113,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { diff --git a/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md b/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md index 32542844..1806acd3 100644 --- a/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md +++ b/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -61,7 +61,7 @@ We'll also need some additional python dependencies ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" diff --git a/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.ipynb b/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.ipynb index b9c3ae86..7f98ac76 100644 --- a/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.ipynb +++ b/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -112,7 +112,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { @@ -158,7 +158,7 @@ "source": [ "SpectralABF gradually learns a better approximation to the coefficients of a basis functions expansion of the free energy of a system, from the generalized mean forces in a similar fashion to the ABF sampling method.\n", "\n", - "For this Colab, we are using butane as the example molecule." + "In this Colab notebook, we will use butane as an example system." ] }, { @@ -435,7 +435,7 @@ "id": "3UrzENm_oo6U" }, "source": [ - "Next, we load PySAGES and the relevant classes and methods for our problem" + "Next, we import PySAGES and the necessary classes and methods for our simulation" ] }, { @@ -485,8 +485,8 @@ "id": "Fz8BfU34pA_N" }, "source": [ - "We now simulate $5\\times10^5$ time steps.\n", - "Make sure to run with GPU support, otherwise, it can take a very long time." + "We will now run a simulation for $5\\times10^5$ time steps.\n", + "For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete." ] }, { diff --git a/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.md b/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.md index 2f002781..bc2444e3 100644 --- a/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.md +++ b/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -61,7 +61,7 @@ We'll also need some additional python dependencies ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" @@ -81,7 +81,7 @@ cd /content/spectral-abf SpectralABF gradually learns a better approximation to the coefficients of a basis functions expansion of the free energy of a system, from the generalized mean forces in a similar fashion to the ABF sampling method. -For this Colab, we are using butane as the example molecule. +In this Colab notebook, we will use butane as an example system. ```python id="BBvC7Spoog82" @@ -346,7 +346,7 @@ def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), s ``` -Next, we load PySAGES and the relevant classes and methods for our problem +Next, we import PySAGES and the necessary classes and methods for our simulation ```python id="fpMg-o8WomAA" @@ -372,8 +372,8 @@ method = SpectralABF(cvs, grid) ``` -We now simulate $5\times10^5$ time steps. -Make sure to run with GPU support, otherwise, it can take a very long time. +We will now run a simulation for $5\times10^5$ time steps. +For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete. ```python colab={"base_uri": "https://localhost:8080/"} id="K951m4BbpUar" outputId="8005b8a9-2967-4eb9-f9db-e0dc0d523835" diff --git a/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.ipynb b/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.ipynb index 803602ea..6cca37d6 100644 --- a/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.ipynb +++ b/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -113,7 +113,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { diff --git a/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.md b/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.md index e34ab57f..f7373819 100644 --- a/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.md +++ b/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -61,7 +61,7 @@ We'll also need some additional python dependencies ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" diff --git a/examples/openmm/harmonic_bias/Harmonic_Bias.ipynb b/examples/openmm/harmonic_bias/Harmonic_Bias.ipynb index c646d936..0e6af0f7 100644 --- a/examples/openmm/harmonic_bias/Harmonic_Bias.ipynb +++ b/examples/openmm/harmonic_bias/Harmonic_Bias.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -88,7 +88,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { @@ -176,12 +176,12 @@ "\n", "T = 298.15 * unit.kelvin\n", "dt = 2.0 * unit.femtoseconds\n", - "pdb_file = \"adp-explicit.pdb\"\n", + "adp_pdb = \"adp-explicit.pdb\"\n", "\n", "\n", - "def generate_simulation(filename=pdb_file, T=T, dt=dt, **kwargs):\n", + "def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt, **kwargs):\n", " \"\"\"\n", - " Generates a simulation context to which will attatch our sampling method.\n", + " Generates a simulation context to which will attach our sampling method.\n", " \"\"\"\n", " pdb = app.PDBFile(pdb_filename)\n", "\n", diff --git a/examples/openmm/harmonic_bias/Harmonic_Bias.md b/examples/openmm/harmonic_bias/Harmonic_Bias.md index 05ada2fc..85f6cecd 100644 --- a/examples/openmm/harmonic_bias/Harmonic_Bias.md +++ b/examples/openmm/harmonic_bias/Harmonic_Bias.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of OpenMM and the openmm-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -53,7 +53,7 @@ os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LI ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" @@ -95,12 +95,12 @@ import openmm.unit as unit T = 298.15 * unit.kelvin dt = 2.0 * unit.femtoseconds -pdb_file = "adp-explicit.pdb" +adp_pdb = "adp-explicit.pdb" -def generate_simulation(filename=pdb_file, T=T, dt=dt, **kwargs): +def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt, **kwargs): """ - Generates a simulation context to which will attatch our sampling method. + Generates a simulation context to which will attach our sampling method. """ pdb = app.PDBFile(pdb_filename) diff --git a/examples/openmm/metad/Metadynamics-ADP.ipynb b/examples/openmm/metad/Metadynamics-ADP.ipynb index 9779b12f..61f5f981 100644 --- a/examples/openmm/metad/Metadynamics-ADP.ipynb +++ b/examples/openmm/metad/Metadynamics-ADP.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -92,7 +92,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { @@ -201,7 +201,7 @@ }, "source": [ "\n", - "Next, we load PySAGES and the relevant classes and methods for our problem\n" + "Next, we import PySAGES and the necessary classes and methods for our simulation\n" ] }, { @@ -273,7 +273,7 @@ }, "source": [ "We now simulate the number of time steps set above.\n", - "Make sure to run with GPU support, otherwise, it can take a very long time.\n", + "For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete.\n", "On the GPU this should run in around half an hour." ] }, diff --git a/examples/openmm/metad/Metadynamics-ADP.md b/examples/openmm/metad/Metadynamics-ADP.md index b30b119a..17e20aee 100644 --- a/examples/openmm/metad/Metadynamics-ADP.md +++ b/examples/openmm/metad/Metadynamics-ADP.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of OpenMM and the openmm-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -53,7 +53,7 @@ os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LI ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" @@ -126,7 +126,7 @@ def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt, **kwargs): -Next, we load PySAGES and the relevant classes and methods for our problem +Next, we import PySAGES and the necessary classes and methods for our simulation @@ -175,7 +175,7 @@ method = Metadynamics(cvs, height, sigma, stride, ngauss, deltaT=deltaT, kB=kB, We now simulate the number of time steps set above. -Make sure to run with GPU support, otherwise, it can take a very long time. +For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete. On the GPU this should run in around half an hour. diff --git a/examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb b/examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb index ee946588..350ef6df 100644 --- a/examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb +++ b/examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -88,7 +88,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { @@ -206,7 +206,7 @@ }, "source": [ "\n", - "Next, we load PySAGES and the relevant classes and methods for our problem\n" + "Next, we import PySAGES and the necessary classes and methods for our simulation\n" ] }, { @@ -280,7 +280,7 @@ }, "source": [ "We now simulate the number of time steps set above.\n", - "Make sure to run with GPU support, otherwise, it can take a very long time.\n", + "For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete.\n", "On the GPU this should run in around half an hour." ] }, diff --git a/examples/openmm/metad/nacl/Metadynamics_NaCl.md b/examples/openmm/metad/nacl/Metadynamics_NaCl.md index 06fc28e6..fe04f726 100644 --- a/examples/openmm/metad/nacl/Metadynamics_NaCl.md +++ b/examples/openmm/metad/nacl/Metadynamics_NaCl.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of OpenMM and the openmm-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -53,7 +53,7 @@ os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LI ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" @@ -134,7 +134,7 @@ def generate_simulation(pdb_filename=nacl_pdb, T=T, dt=dt): -Next, we load PySAGES and the relevant classes and methods for our problem +Next, we import PySAGES and the necessary classes and methods for our simulation @@ -186,7 +186,7 @@ method = Metadynamics(cvs, height, sigma, stride, ngauss, deltaT=deltaT, kB=kB, We now simulate the number of time steps set above. -Make sure to run with GPU support, otherwise, it can take a very long time. +For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete. On the GPU this should run in around half an hour. diff --git a/examples/openmm/spectral_abf/ADP_SpectralABF.ipynb b/examples/openmm/spectral_abf/ADP_SpectralABF.ipynb index c1c3e304..7f400f9d 100644 --- a/examples/openmm/spectral_abf/ADP_SpectralABF.ipynb +++ b/examples/openmm/spectral_abf/ADP_SpectralABF.ipynb @@ -8,8 +8,8 @@ "source": [ "# Setting up the environment\n", "\n", - "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n", - "We download it from Google Drive and make it visible to the running python process in this Colab instance." + "First, we set up our environment. We will be using a pre-compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n", + "It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance." ] }, { @@ -88,7 +88,7 @@ "source": [ "## PySAGES\n", "\n", - "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`." + "Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`." ] }, { @@ -194,7 +194,7 @@ "id": "3UrzENm_oo6U" }, "source": [ - "Next, we load PySAGES and the relevant classes and methods for our problem" + "Next, we import PySAGES and the necessary classes and methods for our simulation" ] }, { @@ -245,7 +245,7 @@ }, "source": [ "We now simulate the number of time steps set above.\n", - "Make sure to run with GPU support, otherwise, it can take a very long time." + "For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete." ] }, { diff --git a/examples/openmm/spectral_abf/ADP_SpectralABF.md b/examples/openmm/spectral_abf/ADP_SpectralABF.md index adeb2fd8..91b6edad 100644 --- a/examples/openmm/spectral_abf/ADP_SpectralABF.md +++ b/examples/openmm/spectral_abf/ADP_SpectralABF.md @@ -16,8 +16,8 @@ jupyter: # Setting up the environment -First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin. -We download it from Google Drive and make it visible to the running python process in this Colab instance. +First, we set up our environment. We will be using a pre-compiled and packaged installation of OpenMM and the openmm-dlext plugin. +It will be downloaded from Google Drive and made accessible to the Python process running in this Colab instance. ```bash id="3eTbKklCnyd_" @@ -53,7 +53,7 @@ os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LI ## PySAGES -The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`. +Next, we install PySAGES. The latest version is retrieved from GitHub and installed (along with its dependencies) using `pip`. ```python id="B-HB9CzioV5j" @@ -122,7 +122,7 @@ def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt, **kwargs): ``` -Next, we load PySAGES and the relevant classes and methods for our problem +Next, we import PySAGES and the necessary classes and methods for our simulation ```python id="fpMg-o8WomAA" @@ -149,7 +149,7 @@ method = SpectralABF(cvs, grid) We now simulate the number of time steps set above. -Make sure to run with GPU support, otherwise, it can take a very long time. +For optimal performance, ensure that the simulation is executed with GPU acceleration. Otherwise, it may take a considerably longer time to complete. ```python id="K951m4BbpUar"