Optimization tasks that are relevant to industry in the fields of materials science and chemistry are typically hierarchical, noisy, multi-fidelity 2,3 , multi-objective , high-dimensional , nonlinearly correlated, and involve mixed numerical and categorical variables subject to linear and non-linear constraints. Existing benchmark datasets have limitations as they ignore or simplify the impact of noise and the occurrence of failure with certain parameter combinations. By integrating simulation failure and heteroskedastic noise, we aim to achieve a "Turing test" scenario where the surrogate model is practically indistinguishable from the ground truth simulation. This strategy bridges the gap between low-cost surrogate functions based on benchmark datasets and the high-cost evaluation of objective functions in real-world scenarios.