A key aspect in the evolution of MP is the publication of material data that are as close as possible to experimental data. For this reason, the fundamental computational pipeline of MP was revamped in 2020 to incorporate the results obtained using the r 2 SCAN metaGGA functional into the database. The objective of using the metaGGA functional is to improve the accuracy of the results on MP with respect to experimental data. However, as was pointed out in section 4, no matter how exhaustive the pipeline is in trying several combination of parameters towards achieving a reasonably low total energy, it is possible that some permutation of parameters might be missed. Instead of seeking exhaustive parameter searches to achieve the lowest possible energy structure, it is better to apply a simple parameter identification method that is based on the initial structure. For example, all the VASP tags can be frozen to values that are generally applicable to a broad range of systems, and the k-point mesh can be identified according to the lengths of the three lattice parameters, such as the procedure applied in the TinyUnitCells dataset in the materialsalchemist repository: if the lattice parameter l i < 4, set the number of kpoints along the i dimension (k i ) to 10; if 4 ≤ l i < 8, k i = 6, otherwise k i = 3. Although this recipe might not yield the perfect ground state, and hence might slightly deviate from the experimental results, it will eliminate the potential inconsistency that was discussed in section 4.