We use the Silhouette score as our metric because its boundedness gives it a natural interpretation in this context. Scores that are too close to zero indicate that no clustering has occured, whereas scores too close to one indicate a disconnected latent space, which might mean the model had failed at satisfying the Gaussian prior. Ideally, therefore, we would observe moderate Silhouette scores, and indeed we do. The silhouette score for each PC pair is calculated by taking subsamples of PC pair vectors and computing their Silhouette Score relative to the known verified-AMP/non-verified-AMP properties. Once this has been computed for all pairs of PC's [1 -5] we then find the pair that has the highest silhouette score.