Finding the optimal positions of atoms in a unit cell is common in computational materials design particularly in property calculation, where high-throughput screening can involve energy minimisation for thousands of different compositions , and crystal structure prediction (CSP), where energy minimisation of thousands of trial structures is performed for each composition . Each such geometry optimisation requires an initial trial configuration and an energy model to update atomic positions and cell geometry to arrive at a local energy minimum structure. This energy minimisation can be performed using a variety of models including classical force fields, machine learning and Density Functional Theory (DFT). Classical or machine-learnt force field models typically require considerable reference data for their derivation and are not widely available and transferable. The DFT approach does not require the derivation of effective interatomic interactions for each chemistry and is typically more accurate but significantly more computationally expensive. The task of performing high-throughput calculations can be accelerated by better choices of the trial structures, particularly for expensive DFT calculations where each poor choice of initial configuration can result in a high-energy structure whose optimisation will be lengthy and/or numerically unstable. The ideal starting point is to place the atoms at interatomic distances informed by the balance of attractive and repulsive interactions. While simple and accurate functional forms to describe these interatomic interactions are not generally available, the nature of these interactions is encoded in the known experimental structures that they produce.