For the ω-CP model, c z 0 (R) is a strictly negative and real quantity for any R. Problems will arise whenever the solution gets near the degeneracy and the c z 0 (R) surrogate returns a positive, albeit small value, which renders the predicted ∆E, and subsequently the gradient component responsible for minimizing the degeneracy, to be zero, since we only take the real This issue can be naively remedied in a noiseless setting by adding training points where nonphysical c z 0 is predicted, or more generally and robustly, even in the noised setting, by considering bounded GPR, which includes the use of warped-GPR, non-Gaussian likelihood or constrained kernel hyperparameter optimization, and may be worth exploring in future work.