These issues become even more pertinent with the growing number of projects relying on machine learning (ML), large scale structure encoding, and automated, robust processes for chemical structure conversion and comparison. It should be noted that as a general use descriptor, the Z-matrix representation is not very useful. Although it is translationally and rotationally invariant, it is permutationally variant. It is, however, still a powerful tool in structure optimisation and related applications. A recent study by Meyer and Hauser highlights the advantages of non-redundant internal coordinate systems in Gaussian process regression minima optimisation, and is equally helpful for transition state search. It also finds applications in several pre-and postprocessing tasks. For example, in the computation of phase corrections for molecular photodynamics calculations, one requires an automated algorithm for the detection of a phase shift. Interpolation of two structures through a Z-matrix can be used for pathological cases where the geometric overlap is close to zero. However, its greatest relevance is in day to day applications, but many codes struggle to handle exceptions when atoms adopt linear conformation and the (back-)conversion (from) to internal coordinates becomes ill-defined. These are regular occurrences when using standard quantum chemistry packages in geometry optimisations under the application of constraints or in more general searches.