Level 3: How to implement your own pair potential in RUMD

This tutorial will describe how to implement a new pair potential and test it. Let us say we want to implement a Gaussian core potential:

  $\displaystyle v(r) = \epsilon \exp\left[ -\left( \frac{r}{\sigma} \right)^2 \right]
$ (3)

When implementing a new pair potential there are a number of things to do:

  1. Implement the potential and force into the RUMD source code
  2. Include the new potential in the Python interface
  3. Compile
  4. Test the potential
  5. Use it

The PairPotential.h in the directory <RUMD-HOME>/include /rumd contains all the pair potentials, implemented as classes in C++.

We start with a review of the general 12-6 Lennard-Jones potential - it is the first pair potential in the file (search for Pot_IPL_12_6). Note that this is not the one most typically used in simulations; that would be one of its derived classes Pot_LJ_12_6. The difference is only in how the parameters are specified: for the Pot_IPL_12_6 potential, rather than Epsilon and Sigma, we specify the coefficients of the IPL terms, A12 and A6. We will make a copy of it and change it to the Gaussian core potential.

The implementation is split into two major parts: The first part is the function SetParams(...) which is called by the user via the python interface. The last part is the function ComputeInteraction where the actual computation (on the GPU) is taking place.

SetParams(...) is called via the python interface once for each type of interaction, with i and j denoting the types of the two particles involved in the particular interaction. The user specifies the coefficients $A_{12}$ and $A_6$ and the cut-off $R_{cut}$ (in other potentials the parameters might include a characteristic length $\sigma$ and characteristic energy $\epsilon$ and possibly more parameters). The parameters of the potential are communicated by storing them in h_params[i][j][]. NOTE: the correctness of the program relies on h_params[i][j][0] containing the cut-off in absolute units. In other words, interactions between particles of type i and j are only computed if the distance between the two particles is less than h_params[i][j][0]. The rest of the stored parameters are chosen to facilitate fast computation.

The actual computation of the potential and the associated force is done on the GPU by the function ComputeInteraction(...). When this is called dist2 contains the square of the distance between the two particles in question, and param contains the appropriate parameters as set up by SetParams(...). From this information, the function calculates $s$ (i.e. the force, see below in equation (5)) and the potential. In the Lennard-Jones potential it is convenient to define two inverse lengths invDist2 and invDist6 to minimize the number of calculations. With these lengths it is easy to calculate $s$ and the potential energy. (*my_f).w += is the contribution to the potential energy.

We will now proceed to implement a Gaussian core potential. First step is to write up the equations we will implement. We will here implement the Gaussian core potential (repeated below)

  $\displaystyle v(r) = \epsilon \exp\left[ -\left( \frac{r}{\sigma} \right)^2 \right]
$ (4)

We have to provide the function $s$ which is used by RUMD to calculate the force:

  $\displaystyle s(r) \equiv -\frac{1}{r}\frac{d U(r)}{d r} = \frac{2 \epsilon}{\sigma^2}\exp\left[ -\left( \frac{r}{\sigma} \right)^2 \right]
$ (5)

Now copy the entire Lennard-Jones potential and paste it somewhere in the file PairPotential.h. Replace Pot_IPL_12_6 with Pot_Gaussian (four places). Choose a default identity string, e.g. SetID_String("potGauss");.

We will change the arguments in the SetParams(...) to match those of the other LJ potential Pot_LJ_12_6 which uses $\epsilon$ and $\sigma$ instead of $A_{12}$ and $A_6$, since the Gaussian potential also has a characteristic length $\sigma$ and a characteristic energy $\epsilon$. But we need to make corresponding changes to the h_params[i][j][]'s. For the Gaussian potential they become:

Note that here the user parameter Rcut is to be interpreted as a scaled cutoff, in units of the corresponding $\sigma$. Now we can write the part taking place on the GPU. Since we do not need any inverse distances delete the lines computing invDist2 and invDist6. We define instead a float called Dist2:
float Dist2 = dist2 * param[1];
which is $(r/\sigma)^2$. To avoid computing the exponential more than once, we define:
float exponential =  exp(-Dist2);
With these definitions, $s$ is simply implemented as:
float s = 2.f * param[1] * param[2] * exponential;
The next line is the potential:
(*my_f).w += param[2] * exponential;

This finishes the actual implementation, and we now proceed with a number of more technical changes ensuring that RUMD knows your new pair potential exists.

If everything compiled without errors the new pair potential will now be available from the python interface, and you need to test it. Run a simulation as described in the Level 1 tutorial, but choose the new potential.

Check that the simulation ran with the correct potential by plotting it by adding
potential.WritePotentials(sim.sample.GetSimulationBox()) to your script. This command produces a file ”potentials_PotID.dat” where the potential $v(r)$ is followed by the radial force $f(r)$ in each column for all possible particle pairs.

Make sure that the cut-off is done correctly by the program by zooming in on that distance. An example is shown in figure 5.

Figure 5: The Gaussian core potential.
Next, run a NVE simulation and test that the total energy is conserved (i.e., have fluctuations that are much smaller than for the potential and kinetic energy).

Have fun with RUMD!