# Practical sessions

Here we’ll gather the material and tasks for the daily sessions. Solutions to the tasks are found here

### Some tips for frequent problems

• The order in which you create and manipulate the system and simulation matters!

Remember that the workflow should always be

system = readdy.ReactionDiffusionSystem(...)
# ... system configuration
simulation = system.simulation(...)
# ... simulation setup
simulation.run(...)
# ... analysis of results


If you made a mistake while registering species, potentials, etc., just create a new system and run the configuration again. The same goes for the simulation, just create a new one. In the jupyter notebooks it is sufficient, to just run all the cells again.

• Simulation box and box potential for confining particles

If you want to confine particles to some cube without periodic boundaries, the box potential must be fully inside the simulation box. Remember that the box is centered around (0,0,0). For example the following will confine A particles to a cube of edge length 10

system = readdy.ReactionDiffusionSystem(
box_size=[12., 12., 12.], unit_system=None,
periodic_boundary_conditions=[False, False, False])
origin = np.array([-5., -5., -5.])
extent = np.array([10., 10., 10.])
"A", force_constant=10.,
origin=origin, extent=extent)


The two vectors origin and extent span a cube in 3D space, the picture you should have in mind is the following

• Initial placement of particles inside a certain volume

If you have already defined origin and extent of your confining box potential, it is easy to generate random positions uniformly in this volume

n_particles = 30
uniform = np.random.uniform(size=(n_particles,3))
init_pos = uniform * extent + origin


Here uniform is a matrix Nx3 where each row is a vector in the unit cube $\in{[0,1]\times[0,1]\times[0,1]}$ . This is multiplied with extent, yielding a uniform cube ${[0,\mathrm{extent}_0]\times[0,\mathrm{extent}_1]\times[0,\mathrm{extent}_2]}$. If you add the origin to this you get this cube at the right position (with respect to our box coordinates centered around (0,0,0)), i.e.

• 2D plane

If you want to confine particles to a 2D plane, just use the box potential but make the extent in one dimension very small, i.e.

system = readdy.ReactionDiffusionSystem(
box_size=[12., 12., 3.], unit_system=None,
periodic_boundary_conditions=[False, False, False])
origin = np.array([-5., -5., -0.01])
extent = np.array([10., 10., 0.02])
"A", force_constant=10.,
origin=origin, extent=extent)


Having defined origin and extent it is now easy to add particles to this 2D plane. Note that I also made the box_size in z direction smaller, however it should be large enough.

• Output file size

Please make use of your /storage/mi/user directories, your home will fill up quicker.

data_dir = "/storage/mi/user" # replace user with your username
simulation.output_file = os.path.join(data_dir, "myfile.h5")


Additionally, use a stride on your observables, e.g.

simulation.record_trajectory(stride=100)
# or
simulation.observe.particle_positions(stride=100)

• Reaction descriptor language

In expressions like

system.reactions.add("fus: A +(2) B-> C", rate=0.1)


the value in the parentheses +(2) is the reaction distance.

• look at VMD output if something is fishy

This might reveal some obvious mistakes. Therefore you must have registered the according observable

simulation.record_trajectory(stride=100) # use appropriate stride
# ... run simulation


Then in a bash shell do

vmd -e myfile.h5.xyz.tcl


or prefix with ! in the jupyter notebook.

Get miniconda and install it

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh


If your home directory is limited in space, you might want to install it under storage, i.e. when prompted for install location, choose either /home/mi/user/miniconda3 or /storage/mi/user/miniconda3 (replace user with your username). Your ~/.bashrc should contain the line

. /storage/mi/user/miniconda3/etc/profile.d/conda.sh


to enable the conda command. If you try out which conda, you should see its function definition, and you’re good to go.

Create a conda environment workshop

conda create -n workshop


and activate it.

conda activate workshop


Add conda-forge as default channel

conda config --add channels conda-forge --env


and install the latest readdy in it

(workshop) $conda install -c readdy readdy  Check if it worked, start a python interpreter and do import readdy print(readdy.__version__)  If this does not return an error, you are ready to readdy (almost). Make sure you also have vmd installed. It should be installed on the universities machines. ### Task 2) ReaDDy introduction presentation Follow along the ReaDDy introduction. You should be able to reproduce the presented usage in your own ipython notebook. ### Task 3) Number of particles Consider the reaction The reaction distance should be$R=1$. Set up a periodic box with dimensions$20\times20\times20$Add 1000 A particles with$D=1$. The diffusion constant of B should be$D=1$. They should be uniformly distributed in the box, use n_particles = 1000 init_pos = np.random.uniform(size=(n_particles,3)) * extent + origin simulation.add_particles("A", init_pos)  Observe the number of particles with a stride of 1. Additionally you can print the current number of particles using the callback functionality simulation.observe.number_of_particles( 1, ["A","B"], callback=lambda x: print("A {}, B {}".format(x[0],x[1])) )  Simulate the system for 10000 steps with time step size 0.01. 3a) Obtain the number of particles and plot them as a function of time. The analytic solution of the concentration of particles subject to the reaction above is obtained by solving the ODE which yields 3b) Fit the function$a(t)$to your counts data, to obtain the parameter$k$. For$a_0$use n_particles. Make use of scipy.optimize.curve_fit. Can the function$a(t)$describe the data well? 3c) Repeat the procedures a) and b) but change the diffusion coefficient to$D=0.01$and change the microscopic reaction rate to$\lambda=1$. What happened to your fit? ### Task 4) Crowded mixture, MSD The time dependent mean squared displacement (MSD) is defined as where$\mathbf{x}_t$is the position of a particle at time$t$, and$\mathbf{x}_0$is its initial position. The difference of these is then squared, yielding a squared travelled distance. This quantity is then averaged over all particles. The task is to set up a crowded mixture of particles and measure the MSD. There is one species A with diffusion coefficient 0.1. The simulation box shall be$20\times20\times20$and non-periodic, i.e. define a box potential that keeps particles inside, e.g. with an extent$19\times19\times19$and appropriate origin. Define a harmonic repulsion between A particles with force constant 100 and interaction distance 2. Add 1000 A particles to the simulation, uniformly distributed within the box potential. Observe the positions of particles with a stride of 1. Run the simulation with a time step size of 0.01 for 20000 steps. In the post-processing you have to calculate the MSD from the observed positions. Implement the following steps: 1. Convert positions list into numpy array$T\times N\times3$2. From every particles position subtract the initial position 3. Square this 4. Average over particles np.mean(...) Since the positions observable returns a list of list, it might come in handy to convert this to a numpy array of shape$T\times N\times3$(step 1). You may use the following brute force method to do this T = len(positions) N = len(positions[0]) pos = np.zeros(shape=(T, N, 3)) for t in range(T): for n in range(N): pos[t, n, 0] = positions[t][n][0] pos[t, n, 1] = positions[t][n][1] pos[t, n, 2] = positions[t][n][2]  You shall implement steps 2.-4. by yourself. 4a) Finally plot the MSD as a function of time in a log-log plot, let’s call this the crowded result. (Hint: You may find that there is an initial jump in the squared displacements. Equilibrating the particle positions before starting the measurement may help you there.) 4b) Repeat the whole procedure, but do not register the harmonic repulsion between particles, this shall be the free result. Compare the MSD to the previous result, possibly in the same plot. 4c) Additionally plot the analytical solution for freely diffusing particles From your resulting plot identify “finite size saturation”, “subdiffusion”, “reduced normal diffusion”, “ballistic/normal diffusion” ### Task 5) Crowded mixture, RDF The radial distribution function (RDF)$g(r)$describes how likely it is to find two particles at distance$r$. Compare the RDF of harmonically repelling particles and the RDF of non-repelling particles. Therefore set up the same system as in Task 4) but this time the system shall be periodic and there is no box potential. You may want to equilibrate the initial particle positions. Instead of observing the particle positions, observe the radial distribution function simulation.observe.rdf(stride=1000, bin_borders=np.arange(0.,10.,0.2), types_count_from="A", types_count_to="A", particle_to_density=n_particles/system.box_volume)  In the post-processing, you shall use _, bin_centers, distribution = traj.read_observable_rdf()  to obtain the observable. The distribution contains multiple$g(r)$, one for each time it was recorded. Average them over time. 5a) Plot the RDF as a function of the distance (i.e. meandistribution as function of bin_centers) 5b) Perform the same procedure but for non-interacting particles, compare with the previous result, preferably in the same plot. What are your observations? # Tuesday - Lotka Volterra This session will cover a well studied reaction diffusion system, the Lotka Volterra system, that describes a predator prey dynamics. We will investigate how parameters in these system affect spatiotemporal correlations. ### Task 1) well mixed Simulate a Lotka-Volterra/predator-prey system for the given parameters and determine the period of oscillation. There are two particle species A (prey) and B (predator) with the same diffusion coefficient$D=0.7$. Both particle species shall be confined to a quadratic 2D plane with an edge length of roughly 100, and non periodic boundaries, i.e. the 2D plane must be fully contained in the simulation box. Choose a very small thickness for the plane, e.g. 0.02, and a force constant of 50. Particles of the same species interact via harmonic repulsion, with a force constant of 50, and an interaction distance of 2. There are three reactions for the Lotka Volterra system: birth, eat, and decay. Initialize a system by randomly positioning 1000 particles of each species on the 2D plane. Run the simulation for 100,000 integration steps with a step size of 0.01. Observe the number of particles and the trajectory, with a stride of 100. From the observable, plot the number of particles for the two species as a function of time in the same plot. Make sure to label the lines accordingly. Additionally plot the phase space trajectory, i.e. plot the number of predator particles against the number of prey particles. Question: What is the period of oscillation? ### Task 2) diffusion influenced We simulate the same system as in Task 1) but now introduce a scaling parameter$\ell$. This scaling parameter is used to control the ratio of reaction rates$\lambda$and the rate of diffusion$D$. For a given parameter$\ell$• change all reaction rate constants$\tilde\lambda= \lambda\times\sqrt{\ell}$• change all diffusion coefficients$\tilde D= D/\sqrt{\ell}$where the tilde ($\tilde\,$) denotes the actually used value in the simulation and the value without tilde is the one from Task 1). This means that the ratio is controlled by$\ell$. Perform the same simulation as in Task 1) but for different scaling parameters$\ell$, each time saving the resulting plots (time series and phase plot) to a file, use plt.savefig(...). Run each simulation for$n$number of integration steps, where Vary the scaling parameter from 1 to 400. Question: For which$\ell$do you see a qualitative change of behavior in the system, both from looking at the plots and the VMD visualization? Which cases are reaction-limited and which cases are diffusion-limited? ### Task 3) Starting from the parameters of a diffusion influenced system from Task 2), set up a simulation, where prey particles form a traveling wavefront, closely followed by predators. Therefore you might want to change the simulation box and box potential parameters to get a 2D rectangular plane. Feel free to adjust all parameters. You can experiment with other spatial patterns as well, have a look at this paper. # Wednesday - Self assembly This session will cover macromolecules and how they assemble into superstructures ### Task 1) polymer and radius of gyration In this task we will calculate the radius of gyration of a polymer as a function of the stiffness of the polymer. We will only need one particle species monomer with diffusion constant$D=0.1$. Note that this will not be a normal species but will be a topology species: system.add_topology_species("monomer", 0.1)  The simulation box shall have box_size= [32.,32.,32.], and be non-periodic. This means that there has to be a box potential registered for the type monomer with force constant 50, origin=np.array([-15,-15,-15]) and extent=np.array([30,30,30]). Between monomers there should be a harmonic repulsion potential with force_constant=50 and interaction_distance=1 In order to build a polymer we use topologies. At first we need to register a type of toplogy system.topologies.add_type("polymer")  The monomers in a polymer must be held together by harmonic bonds, defined by pairs of particle types system.topologies.configure_harmonic_bond( "monomer", "monomer", force_constant=100., length=1.)  For this example we also specify an angular potential, that is defined for a triplet of particle types system.topologies.configure_harmonic_angle( "monomer", "monomer", "monomer", force_constant=stiffness, equilibrium_angle=np.pi)  where an equilibrium_angle$=\pi$means 180 degrees. The next step is creating the simulation. Then we specify the observables, the trajectory and the particle positions simulation.record_trajectory(stride=10000) simulation.observe.particle_positions(stride=1000)  Now we need to set the initial positions of particles and set up the polymer connectivity, therefore we do a 3D random walk that generates us the initial positions of the chain of particles n_particles = 50 init_pos = [np.zeros(3)] for i in range(1, n_particles): displacement = np.random.normal(size=(3)) # normalize the random vetor displacement /= np.sqrt(np.sum(displacement * displacement)) # append the new position to the chain init_pos.append(init_pos[i-1]+displacement) init_pos = np.array(init_pos)  Now that we have generated the positions we have to add them to the simulation together with the actual topology instance top = sim.add_topology("polymer", len(init_pos)*["monomer"], init_pos)  The last step is to define the connectivity for i in range(1, n_particles): top.get_graph().add_edge(i-1, i)  Now that we have defined the simulation object we can run the simulation if os.path.exists(sim.output_file): os.remove(sim.output_file) sim.run(1000000, 2e-2)  1a) Implement the simulation described above for stiffness=0.1. From the recorded trajectory, have a look at the VMD output. Does the structure of the polymer change in the initial phase? 1b) Do the same simulation as above, but now calculate the radius of gyration as a function of time. Given the positions observable, you can calculate the radius of gyration as follows (the assertion statements may help you understand how the arrays are shaped): times, positions = traj.read_observable_particle_positions() # convert to numpy array T = len(positions) N = len(positions[0]) pos = np.zeros(shape=(T, N, 3)) for t in range(T): for n in range(N): pos[t, n, 0] = positions[t][n][0] pos[t, n, 1] = positions[t][n][1] pos[t, n, 2] = positions[t][n][2] # calculate radius of gyration mean_pos = np.mean(pos, axis=1) difference = np.zeros_like(pos) for i in range(n_particles): difference[:,i] = pos[:,i] - mean_pos assert difference.shape == (T,N,3) # square and sum over coordinates (axis=2) squared_radius_g = np.sum(difference * difference, axis=2) assert squared_radius_g.shape == (T,N) # average over particles (axis=1) squared_radius_g = np.mean(squared_radius_g, axis=1) radius_g = np.sqrt(squared_radius_g) assert radius_g.shape == times.shape == (T,)  Plot the radius of gyration as a function of time, is it equilibrated? Calculate its time average and standard deviation over the whole timeseries. 1c) Put all of the above into one function simulate(stiffness) that performs the whole simulation and analysis for a given stiffness parameter and returns the mean (one number only, not an array) and standard deviation of the radius of gyration. The function body should roughly consist of def simulate(stiffness): system = readdy.ReactionDiffusionSystem(...) ... # system configuration simulation = system.simulation(...) out_file = os.path.join(data_dir, "polymer_"+str(stiffness)+".h5") ... # simulation setup simulation.run(...) ... # calculation of radius of gyration return mean_radius_of_gyration, deviation_radius_of_gyration  Measure the mean radius of gyration with standard deviation as a function of the stiffness parameter for values [0.1, 0.5, 1.0, 5.0, 10.]. This might look like: values = [0.1, 0.5, 1., 5., 10.] rgs = [] devs = [] for stiffness in values: rg, dev = simulate(stiffness) rgs.append(rg) devs.append(dev)  Plot the mean radius of gyration as a function of the stiffness. Explain your result by looking also at the VMD output. ### Task 2) linear filament assembly In this task we will also look at a linear polymer chain, but this time we will not place it initially, but instead we will let it self-assemble from monomers in solution. The polymer that we will build will have the following structure head--core--(...)--core--tail  where (...) means that there can be many core particles, but the structure is always linear. The simulation box is periodic with box_size=[20., 20., 20.]. We define three topology particle species and one normal particle species system.add_species("substrate", 0.1) system.add_topology_species("head", 0.1) system.add_topology_species("core", 0.1) system.add_topology_species("tail", 0.1)  We also define one topology type system.topologies.add_type("filament")  There should be the following potentials for topology particles system.topologies.configure_harmonic_bond( "head", "core", force_constant=100, length=1.) system.topologies.configure_harmonic_bond( "core", "core", force_constant=100, length=1.) system.topologies.configure_harmonic_bond( "core", "tail", force_constant=100, length=1.)  Like in task 1) the polymer should be stiff, we can compactly write this for all triplets of particle types: triplets = [ ("head", "core", "core"), ("core", "core", "core"), ("core", "core", "tail"), ("head", "core", "tail") ] for (t1, t2, t3) in triplets: system.topologies.configure_harmonic_angle( t1, t2, t3, force_constant=50., equilibrium_angle=np.pi)  We now introduce a topology reaction. They allow changes to the graph of a topology in form of a reaction. Here we will use the following definition. system.topologies.add_spatial_reaction( "attach: filament(head) + (substrate) -> filament(core--head)", rate=5.0, radius=1.5 )  Using the documentation, familiarize yourself, what this means. Do not hesitate to ask, since topology reactions can become quite a tricky concept! Next create a simulation object. We want to observe the following simulation.record_trajectory(stride=100) simulation.observe.topologies(stride=100)  Add one filament topology to the simulation init_top_pos = np.array([ [ 1. ,0. ,0.], [ 0. ,0. ,0.], [-1. ,0. ,0.] ]) top = simulation.add_topology( "filament", ["head", "core", "tail"], init_top_pos) top.get_graph().add_edge(0, 1) top.get_graph().add_edge(1, 2)  Additionally we need substrate particles, that can attach themselves to the filament n_substrate = 300 origin = np.array([-10., -10., -10.]) extent = np.array([20., 20., 20.]) init_pos = np.random.uniform(size=(n_substrate,3)) * extent + origin simulation.add_particles("substrate", positions=init_pos)  Then, run the simulation if os.path.exists(simulation.output_file): os.remove(simulation.output_file) dt = 5e-3 simulation.run(400000, dt)  One important observable will be the length of the filament as a function of time. It can be obtained from the trajectory as follows: times, topology_records = traj.read_observable_topologies() chain_length = [ len(tops[0].particles) for tops in topology_records ]  The last line is a list comprehension. tops is a list of topologies for a given time step. Hence, tops[0] is the first (and in this case, the only) topology in the system. tops[0].particles is a list of particles belonging to this topology. Thus, its length yields the length of the filament. 2a) Have a look at the VMD output. Describe what happens? Additionally plot the length of the filament as a function of time. Note that you shall now plot the simulation time and not the time step indices, i.e. do the following times = np.array(times) * dt  where dt is the time step size. 2b) Using your data of the filament-length, fit a function of the form to your data. You should use scipy.optimize.curve_fit to do so import scipy.optimize as so def func(t, a, b): return a*(1. - np.exp(-b * t)) + 3. popt, pcov = so.curve_fit(func, times, chain_length) print("popt", popt) print("pcov", pcov) f = lambda t: func(t, popt[0], popt[1]) plt.plot(times, chain_length, label="data") plt.plot(times, f(times), label=r"fit$f(t)=a(1-e^{-bt})+3$") plt.xlabel("Time") plt.ylabel("Filament length") plt.legend() plt.show()  Question: Given the result of the fitting parameters • How large is the equilibration rate? • What will be the length of the filament for$t\to\infty$? 2c) We now introduce a disassembly reaction for the tail particle. This is done by adding the following to your system configuration. def rate_function(topology): """ if the topology has at least (head, core, tail) the tail shall be removed with a fixed probability per time """ vertices = topology.get_graph().get_vertices() if len(vertices) > 3: return 0.05 else: return 0. def reaction_function(topology): """ find the tail and remove it, and make the adjacent core particle the new tail """ recipe = readdy.StructuralReactionRecipe(topology) vertices = topology.get_graph().get_vertices() tail_idx = None adjacent_core_idx = None for v in vertices: if topology.particle_type_of_vertex(v) == "tail": adjacent_core_idx = v.neighbors()[0].get().particle_index tail_idx = v.particle_index recipe.separate_vertex(tail_idx) recipe.change_particle_type(tail_idx, "substrate") recipe.change_particle_type(adjacent_core_idx, "tail") return recipe system.topologies.add_structural_reaction( "filament", reaction_function=reaction_function, rate_function=rate_function)  Familiarize yourself with this kind of structural topology reaction Repeat the same analysis as before, and also observe your VMD output. • How large is the equilibration rate? • What will be the length of the filament for$t\to\infty$? # Thursday - Custom potentials ### Task 0) Implementing a C++ potential Make sure you are within a conda environment that has readdy installed, say workshop. Clone the template for a custom potential into a directory called mypotential git clone https://github.com/chrisfroe/custom-potential-template.git mypotential cd mypotential git submodule init git submodule update  This should give you the following directory structure mypotential ├── binding.cpp ├── cmake │ └── Modules │ └── FindREADDY.cmake ├── CMakeLists.txt ├── run.py ├── setup.py └── pybind11/  Implement the potential in binding.cpp and build it (workshop)$ python setup.py develop


Make sure that it works, run the following short python script:

import numpy as np
import mypot as mp

# use the potential you implemented
#system.potentials.add_custom_pair("A", "A", mp.MyPairPotential, 1., 1., 1.)
sim = system.simulation(kernel="SingleCPU")

sim.run(1000, .1)


.

### Task 1) External potential, double well

1a) Consider the external potential

that only depends on the x-coordinate, $a,b>0$.

Plot this potential in the range $[-2b,+2b]$ for $a=4$ and $b=5$. How will particles behave in this potential?

1b) Implement $U$ as MyExternalPotential. The according force on a particle is then given by

Note that the force only has a x-component. Y and z component are 0. Hints for the binding.cpp:

• The MyExternalPotential C++ class needs two parameters forceConst ($a$) and distance ($b$), implemented as private members
• These are given to the class as constructor arguments
• The PYBIND_MODULE section also needs to accomodate to the two arguments
// binding.cpp
public:
explicit MyExternalPotential(
ParticleType ptype,
: PotentialOrder1(ptype),
forceConst(forceConst),
distance(distance) { }
// ...
private:
};

PYBIND11_MODULE (mypot, m) {

}


The // ... denotes the parts you have to fill in.

Once you are done implementing it, you have to compile it

(workshop) $python setup.py develop  If this returns no errors the potential has been built, and the according module was made available to your (workshop) environment. 1c) You can now go to your jupyter notebook and set up a system. The simulation box will have box_size=[30, 30, 30] and will be periodic. There will be one species A with diffusion_constant=0.1. The only potential in the system will be the one you implemented: system.potentials.add_custom_external("A", mp.MyExternalPotential, 4., 5.)  The last two arguments are forwarded to the C++ constructor, i.e. they are forceConst and distance. Simulate the system for 1000 particles initially placed in the origin (0,0,0) simulation.add_particles("A", np.zeros((1000,3)))  Observe the trajectory and the particle positions simulation.record_trajectory(100) simulation.observe.particle_positions(100)  Run the simulation for 100000 steps and a timestep of$\tau=0.05$. Have a look at the VMD output, do your observations match your expectations? 1d) Make a histogram of all x-positions. Therefore use the positions observable in the following way times, positions = traj.read_observable_particle_positions() # convert to numpy array T = len(positions) N = len(positions[0]) pos = np.zeros(shape=(T, N, 3)) for t in range(T): for n in range(N): pos[t, n, 0] = positions[t][n][0] pos[t, n, 1] = positions[t][n][1] pos[t, n, 2] = positions[t][n][2] # only look at x xs = pos[:,:,0].flatten() # make the histogram hist, bin_edges = np.histogram( xs, bins=np.arange(-15.,15.,0.5), density=True) # from the bin_edges, calculate the bin_centers bin_centers = [] for i in range(len(bin_edges)-1): bin_centers.append(0.5 * (bin_edges[i] + bin_edges[i+1])) bin_centers = np.array(bin_centers)  Plot the histogram hist as a function of the x-coordinates bin_centers. What is the ratio of probability of being in the transition state$x=0$over the probability of being in one of the ground states$x=b$? 1e) In statistical mechanics, the above histogram describes a probability distribution$\rho(x)$. When a system is closed, isolated and coupled to a heat bath with temperature$T$, one can assume that the distribution is related to the potential$U$in the following way: where$k_BT=1$in our case. Using this assumption, try to recover your potential, i.e. solve the above equation for$U$, and calculate it as a function of x, given your histogram. Compare this result for$U$against the true potential you have implemented. What could be sources of error here? ### Task 2) Pair potential, nuclear waffles Have a look at this paper. There are two species N and P with potentials, with the following constants Symbol Value$a110\,\mathrm{MeV}b-26\,\mathrm{MeV}c24\,\mathrm{MeV}\Lambda1.25\,\mathrm{fm}^2\lambda10\,\mathrm{fm}\alpha1.439836\times 10^2\,\mathrm{eV\,fm}$(changed on purpose) The latter part of$V_{PP}$is a screened electrostatic potential already available in ReaDDy, see here. The gaussian potentials however are not implemented in ReaDDy. This is your task. 2a) Implement the pair potential as MyPairPotential.$A$is the prefactor regulating the strength and the sign of interaction ($A>0$repulsive,$A<0$attractive).$L$is the variance, that determines the width of the potential.$r_0$is the cutoff. Use these names also in your binding.cpp for the constructor arguments. You might want to have a look at how this potential actually looks like. Note: From the potential$U(r)$you first have to derive the analytical expression for the force$F(\mathbf{x}_j- \mathbf{x}_i)$acting between two particles located at$\mathbf{x}_i$and$\mathbf{x}_j$. The following identities may help you to calculate the force. When you are done implementing, compile it (workshop)$ python setup.py develop


2b) Now make use of your pair potential in a jupyter notebook.

Consider a periodic system with box_size=[200., 200., 8.]. There should be the two species N and P, standing for neutrons and protons. Both with diffusion_constant=0.1.

There shall be a 2D plane, but this shall extend the simulation box in x and y.

# box, keeping particles on the 2d plane
particle_type="N", force_constant=100.,
origin=[-110., -110., -0.01], extent=[220., 220., 0.02])
particle_type="P", force_constant=100.,
origin=[-110., -110., -0.01], extent=[220., 220., 0.02])


Now configure the custom potentials, i.e. $V_{NN}$, $V_{PP}$ and $V_{NP}$ mentioned above. Therefore use the following constants

a = 110.
b = -26.
c = 24.
variance = 1.25
l = 10.
cutoff = 3.5
alpha = 1.439e2
proton_fraction = 0.4
total_n = 1357 * 4
n_p = int(total_n * proton_fraction)
n_n = int(total_n * (1. - proton_fraction))


# the custom potentials
"N", "P", mp.MyPairPotential, a, variance, cutoff)
"N", "P", mp.MyPairPotential, b-c, 2.*variance, cutoff)

"N", "N", mp.MyPairPotential, a, variance, cutoff)
"N", "N", mp.MyPairPotential, b+c, 2.*variance, cutoff)

"P", "P", mp.MyPairPotential, a, variance, cutoff)
"P", "P", mp.MyPairPotential, b-c, 2.*variance, cutoff)
"P", "P", electrostatic_strength=alpha, inverse_screening_depth=1/l,
repulsion_strength=0., repulsion_distance=1., exponent=1, cutoff=2.*cutoff
)


Compare this with $V_{NN}$, $V_{PP}$ and $V_{NP}$ and validate that I did not make an error here.

Next is the simulation: use the CPU kernel. Observe the trajectory with a stride of 10, and the radial distribution function for the separate pairs

sim.observe.rdf(
100, bin_borders=np.arange(0.,25.,0.1),
types_count_from=["P"], types_count_to=["P"],
particle_to_density=float(n_p)*3./200./200.,
save={"name": "PP", "chunk_size":100}
)
sim.observe.rdf(
100, bin_borders=np.arange(0.,25.,0.1),
types_count_from=["N"], types_count_to=["N"],
particle_to_density=float(n_n)*3./200./200.,
save={"name": "NN", "chunk_size":100}
)
sim.observe.rdf(
100, bin_borders=np.arange(0.,25.,0.1),
types_count_from=["N"], types_count_to=["P"],
particle_to_density=float(n_p)*3./200./200.,
save={"name": "NP", "chunk_size":100}
)


Add particles to the system. There shall be a certain amount of protons. n_n and n_p were defined above.

origin = np.array([-100., -100., -0.01])
extent = np.array([200., 200., 0.02])

init_pos = np.random.uniform(size=(n_p,3)) * extent + origin

init_pos = np.random.uniform(size=(n_n,3)) * extent + origin


Finally run the simulation for 20000 steps and a time step size of 0.05

2c) Obtain the radial distribution functions that were saved under different names. Average them over the time axis (np.mean(...)). Plot them all in the same plot. Note: Since we were calculating the RDF on a 2D plane, we have to correct for this factor. This means plot the distributions as follows

# note the additional " * bin_centers "
plt.plot(bin_centers, mean_distribution * bin_centers)

• How long are position correlations in this system, in absolute lengths?
• How many peaks are in the RDF of PP?
• What is the apparent collision radius of the P particles only considering their PP correlation?
• What is the apparent collision radius of the N particles only considering their NN correlations?

2d) Also observe your VMD output. Can you see waffles? What are the differences of how Schneider et al in their paper simulated the system and what we did? Is ReaDDy an appropriate tool for such a system?