Specifically, we observe a significant reduction in our model's uncertainty for HSFP configurations (Figure ) compared to those for Sella (Figure ), IRC (Figure ), and MD (Figure ). This reduction can be attributed to two main factors: (1) all HFSP configurations until iteration 2 are labeled using DFT and (2) the harmonic potentials used to generate the HFSP configurations result in configurations that are relatively consistent throughout the training process. Utilizing all HFSP configurations for DFT labeling in the early model iterations is particularly advantageous, as Sella, our TS optimizer, relies on finite-differences to generate approximate Hessians to optimize HFSP TS guesses to a first-order saddle point.