The first technique is to rely on random sampling, with no guarantee of uniformity, but which should converge for a large 𝑛. To generate random 3D unit vectors, one approach is to draw random vectors in the corresponding cube, rejecting the points that are not inside the sphere. A simple normalization of the remaining vectors gives a random sampling of the sphere surface. The same result can also be achieved by generating three normally distributed random values and normalizing the vector obtained by these numbers. Another technique to obtain a uniform distribution is to imagine using a simulation of 𝑛 charged points on the surface of a sphere and minimize their electrostatic repulsion. This method, based on the Thomson problem, relies on numerical optimization and can become very expensive if 𝑛 is high. The method we found the most efficient (slightly faster) for the typical values of 𝑛 that we consider (between 100 and 300,000), consists in wrapping a string of points around the sphere in a spiral manner. This technique is closer from laying a uniform grid over the surface than a "random sampling" of the surface, which avoids redundant sampling points. The height ℎ = 𝑟 cos 𝜑 (where (𝑟,𝜃, 𝜑) are the spherical coordinates) of the sphere is uniformly divided into 𝑛 points, for each of these height we chose an angle 𝜃 in the orthogonal plane space so that the difference between two consecutive angles is the golden number. This method is referred to as the spherical Fibonacci mapping. While it is not the optimal solution to the Thomson problem, its uniformity is rather good for our purposes (we have checked that its influence on the calculated properties is negligible) and the computational cost is lower. It also gives a convergence for values of 𝑛 smaller than the random distribution methods described above, allowing us to user smaller sampling sizes, which is why we used this method for the rest of the surface sampling simulations presented in this article.