# Implementation of the potential¶

We start by creating a new package using cargo:

cargo new potential
cd potential


Open Cargo.toml and add the lines

[dependencies]
lumol = {git = "https://github.com/lumol-org/lumol"}


to add the lumol crate as a dependency to the package. To test if everything works, run cargo build and check if an error occurs.

## Defining the struct¶

For the first part of the tutorial, the complete code will be written into the lib.rs file.

The energy function of the Mie potential reads

$u(x) = \varepsilon \frac{n}{n-m} \left(\frac{n}{m}\right)^{\frac{m}{n-m}} \left[ \left( \frac{\sigma}{x}\right)^n - \left( \frac{\sigma}{x}\right)^m \right]$

where $$x$$ denotes the distance between two interaction sites $$i, j$$, with $$x = x_{ij} = | \mathbf{r}_j - \mathbf{r}_i |$$. The parameters of the potential are

• $$n, m$$ the repulsive and attractive exponents, respectively,
• $$\varepsilon$$ the energetic paramater,
• $$\sigma$$ the particle diameter or structural parameter.

We start by defining the struct for our potential. Add the following lines to lib.rs:

use lumol::energy::{Potential, PairPotential};

#[derive(Clone, Copy)]
pub struct Mie {
/// Distance constant
sigma: f64,
/// Exponent of repulsive contribution
n: f64,
/// Exponent of attractive contribution
m: f64,
/// Energetic prefactor computed from the exponents and epsilon
prefactor: f64,
}


In the first two lines we define our imports from Lumol, following with our Mie structure. Notice that we don’t store the epsilon value, instead we store an energetic prefactor that will make it easier to compute the potential.

$\text{prefactor} = \varepsilon \frac{n}{n-m} \left(\frac{n}{m}\right)^{m/(n-m)}$

Next, we implement a constructor function. That’s usefull in this case since we want to compute the prefactor of the potential once before we start our simulation.

In Rust we typically use new for the constructors’ name.

impl Mie {
pub fn new(sigma: f64, epsilon: f64, n: f64, m: f64) -> Mie {
if m >= n {
panic!("The repulsive exponent n has to be larger than the attractive exponent m")
};
let prefactor = n / (n - m) * (n / m).powf(m / (n - m)) * epsilon;
return Mie {
sigma: sigma,
n: n,
m: m,
prefactor: prefactor,
}
}
}


Our function takes the parameter set as input, computes the prefactor and returns a Mie struct. Notice that it panics, for n smaller than or equal to m. The next step is to implement the Potential trait for Mie.

## Implementing Potential¶

Add the following lines below the structs implementation.

impl Potential for Mie {
fn energy(&self, r: f64) -> f64 {
let sigma_r = self.sigma / r;
let repulsive = f64::powf(sigma_r, self.n);
let attractive = f64::powf(sigma_r, self.m);
return self.prefactor * (repulsive - attractive);
}

fn force(&self, r: f64) -> f64 {
let sigma_r = self.sigma / r;
let repulsive = f64::powf(sigma_r, self.n);
let attractive = f64::powf(sigma_r, self.m);
return -self.prefactor * (self.n * repulsive - self.m * attractive) / r;
}
}


energy is the implementation of the Mie potential equation shown above. force is the negative derivative of the energy with respect to the distance, r. To be more precise, the vectorial force can readily be computed by multiplying the result of force with the connection vector $$\vec{r}$$.

The next step is to make our Potential usable in Lumol’s algorithms to compute non-bonded energies and forces. Therefore, we have to implement the PairPotential trait.

## Implementing PairPotential¶

Let’s inspect the documentation for PairPotential.

pub trait PairPotential: Potential + BoxClonePair {
fn tail_energy(&self, cutoff: f64) -> f64;
fn tail_virial(&self, cutoff: f64) -> f64;

fn virial(&self, r: &Vector3D) -> Matrix3 { ... }
}


First, we can see that PairPotential enforces the implementation of Potential which is denoted by pub trait PairPotential: Potential ... (we ignore BoxClonePair for now, as it is automatically implemented for us if we implement PairPotential manually). That makes sense from a didactive point of view since we said that PairPotential is a “specialization” of Potential and furthermore, we can make use of all functions that we had to implement for Potential.

There are three functions in the PairPotential trait. The first two functions start with tail_. These are functions to compute long range or tail corrections. Often, we introduce a cutoff distance into our potential beyond which we set the energy to zero. Doing so we intoduce an error which we can account for using a tail correction. We need two of these corrections, one for the energy, tail_energy, and one for the pressure (which uses tail_virial under the hood). For a beautiful derivation of tail corrections for truncated potentials, see here.

The third function, virial, already has its body implemented – we don’t have to write an implementation for our potential.

We will omit the derivation of the formulae for tail corrections here but they are computed by solving these equations

$\text{tail energy} = \int_{r_c}^{\infty} u(r) r^2 \mathrm{d}r$
$\text{tail virial} = \int_{r_c}^{\infty} \frac{\partial u(r)}{\partial r} r^3 \mathrm{d}r$

The implementation looks like so

impl PairPotential for Mie {
fn tail_energy(&self, cutoff: f64) -> f64 {
if self.m < 3.0 {
return 0.0;
};
let sigma_rc = self.sigma / cutoff;
let n_3 = self.n - 3.0;
let m_3 = self.m - 3.0;
let repulsive = f64::powf(sigma_rc, n_3);
let attractive = f64::powf(sigma_rc, m_3);
return -self.prefactor * self.sigma.powi(3) * (repulsive / n_3 - attractive / m_3);
}

fn tail_virial(&self, cutoff: f64) -> f64 {
if self.m < 3.0 {
return 0.0;
};
let sigma_rc = self.sigma / cutoff;
let n_3 = self.n - 3.0;
let m_3 = self.m - 3.0;
let repulsive = f64::powf(sigma_rc, n_3);
let attractive = f64::powf(sigma_rc, m_3);
return -self.prefactor * self.sigma.powi(3) * (repulsive * self.n / n_3 - attractive * self.m / m_3);
}
}


Note that we cannot correct every kind of energy function. In fact, the potential has to be a short ranged potential. For our Mie potential, both the exponents have to be larger than 3.0 else our potential will be long ranged and the integral that has to be solved to compute the tail corrections diverges. We return zero in that case.

## Running a simulation¶

That concludes the first part. To test your new and shiny potential, you can run a small simulation. You’ll find a minimal Monte Carlo simulation example in the tutorials/potential directory of the main lumol repository where you will also find the src/lib.rs file we created in this tutorial. You can then run the simulation via

cargo run --release


Fantastic! You implemented a new potential and ran a simulation with it!

If you want to share your implementation with other Lumol users only some small additional steps are neccessary. We will talk about them in the next part of this tutorial (which is not yet written).