A limitation of GPs is their computational complexity, which scales cubically, O(𝑛 3 ), with the number of training points 𝑛. In the context of computational materials science, we frequently encounter large candidate libraries-ranging from thousands to tens of thousands of potential materialsmaking naive GP regression computationally prohibitive. To address this issue, we employ a sparse GP approximation using inducing points. These are a smaller set of m points (where 𝑚 ≪ 𝑛) that approximate the full GP. Let Z be the set of m IPs, so that 𝑍 = {𝑧 1 , 𝑧 2 , … , 𝑧 𝑚 }.