The remainder of the paper consists of a brief review of regression using PIPs. The fragmented-basis and the many-body PIPs approaches are then given. Computational details, beginning with the generation of the dataset for training and testing are presented followed by numerical details of the two PIPs approaches. The results begin with the usual precision metrics, followed by an examination of numerous properties of the PESs, with a focus on chain flexibility. Timings for both PESs are detailed. This is followed by a discussion of the two PIP approaches and comments about related atom-centered ML methods. A short summary and conclusions completes the paper.