The training data for the CrabNet model consists only of the compositions and the measured property values (in this case, critical transition temperature). peak_proxy and dens_proxy come into play when assessing the trade-offs between performance and novelty. This can be visualized via a Pareto front (performance vs. novelty), which exists only as a visualization tool in this work, and is a complement to the scaled and weighted sums of performance novelty.