The silica training set includes energy, charge and geometry matching based items. 73% of the items require energy minimization. As shown in Fig. , unlike the Cobalt case, the error fluctuates more between iterations, possibly because of unstable geometries as parameters are converging (note that here the number of parameters to be optimized are significantly higher than the Cobalt case) and the presence of geometry matching items in the training set. Although the single point evaluation based surrogate model ignores the simulation cell optimization items, the proposed method is able to minimize the error comparable to that of black box methods, while taking a fraction of their execution times.