Is E h a valid training target for machine learning? DFT-computed properties that are obtained by direct DFT computation using the material structure such as the formation energy are consistent, steady functions. That is, given that all input parameters are kept fixed, there is a unique value for any given structure (one-to-one or many-to-one) within standardised convergence criteria. However, as we have established above, E h lacks consistency because it is potentially subject to changes as more materials are discovered (potentially a one-to-many relation); that is, it resembles a many-valued function. Hence, the value of E h can at best be used for qualitative assessment of stability, rather than as absolute determinant of stability. Along similar lines, the determination of the voltage is also subject to variation, because it is dependent on the identification of the known most stable phases within the chemical subspaces. Therefore, the average voltage data in the MP database can also suffer from inconsistency, and therefore are not suitable targets for machine learning.