Each filtered transition state guess is then optimized using Sella, our efficient optimizer. Sella constructs an approximate Hessian to optimize the guess structures to a valid TS, i.e., first-order saddle points on the underlying potential energy surface. If the MLP/TS is successfully obtained, we utilize the intrinsic reaction coordinate (IRC) method to confirm that the MLP/TS geometry connects the intended reactants and products. For the purposes of this study, we augmented the Pynta workflow with the ability to run short, 0.5 ps NVT Langevin molecular dynamics simulations at 298 K using a 0.5 fs time-step to fur-ther expand the diversity of our dataset which has been shown to improve rMLP training. Specifically, MLP/MD runs are conducted starting from harmonically-filtered MLP/HFSP configurations in the initial iterations and from Sella-refined MLP/TS configurations in later iterations (see Figure ).