# Entropy fingerprint¶

The entropy parameter was introduced by Piaggi et al for identification of defects and distinction between solid and liquid. The entropy paramater $$s_s^i$$ is defined as,

$s_s^i = -2\pi\rho k_B \int_0^{r_m} [g_m^i(r)\ln g_m^i(r) - g_m^i(r) + 1] r^2 dr$

where $$r_m$$ is the upper bound of integration and $$g_m^i$$ is radial distribution function centered on atom $$i$$,

$g_m^i(r) = \frac{1}{4\pi\rho r^2} \sum_j \frac{1}{\sqrt{2\pi\sigma^2}} \exp{-(r-r_{ij})^2/(2\sigma^2)}$

$$r_{ij}$$ is the interatomic distance between atom $$i$$ and its neighbors $$j$$ and $$\sigma$$ is a broadening parameter.

The averaged version of entropy parameters $$\bar{s}_s^i$$ can be calculated in two ways, either using a simple averaging over the neighbors given by,

$\bar{s}_s^i = \frac{\sum_j s_s^j + s_s^i}{N + 1}$

or using a switching function as described below,

$\bar{s}_s^i = \frac{\sum_j s_s^i f(r_{ij}) + s_s^i}{\sum_j f(r_{ij}) + 1}$

$$f(r_{ij})$$ is a switching parameter which depends on $$r_a$$ which is the cutoff distance. The switching function shows a value of 1 for $$r_{ij} << r_a$$ and 0 for $$r_{ij} >> r_a$$. The switching function is given by,

$f(r_{ij}) = \frac{1-(r_{ij}/r_a)^N}{1-(r_{ij}/r_a)^M}$

Entropy parameters can be calculated in pyscal using the following code,

import pyscal.core as pc
sys = pc.System()
sys.find_neighbors(method="cutoff", cutoff=0)
lattice_constant=4.00
sys.calculate_entropy(1.4*lattice_constant, averaged=True)
atoms = sys.atoms
entropy = [atom.entropy for atom in atoms]
average_entropy = [atom.avg_entropy for atom in atoms]


The value of $$r_m$$ is provided in units of lattice constant. Further parameters shown above, such as $$\sigma$$ can be specified using the various keyword arguments. The above code does a simple averaging over neighbors. The switching function can be used by,

sys.calculate_entropy(1.4*lattice_constant, ra=0.9*lattice_constant, switching_function=True, averaged=True)


In pyscal, a slightly different version of $$s_s^i$$ is calculated. This is given by,

$s_s^i = -\rho \int_0^{r_m} [g_m^i(r)\ln g_m^i(r) - g_m^i(r) + 1] r^2 dr$

The prefactor $$2\pi k_B$$ is dropped in the entropy values calculated in pyscal.