They usually have more complex PES than simple substitutions and are suitable for RMLPs to capture the PES shape. In 2015, Gastegger and Marquetand established a Behler type HDNN for a classic Claisen rearrangement, which is considered as the first NNP for organic reactions . They sampled 17k data from metadynamics with DFT for training and validation. Their model captures the overall shape of the reaction MEP, but the network highly overestimated the barrier by 4.06 kcal/mol. In 2022, an interesting study was conducted to compare three data-efficient models, GAP, ACE, and NequIP, on a basic Diels-Alder reaction PES . Unlike many RMLPs requiring tens of thousands data points for a single reaction, they found these methods only requires 500, 100, 100 of training data to reach accuracies within 1 kcal/mol error. In addition, many other reactive RMLPs have been constructed by GAP, PhysNet, SchNet models with active learning strategy to describe accurate PES for Diels-Alder, Claisen and other pericyclic reactions . These RMLPs are further applied in studying dynamic properties such as trajectory analysis, free energy and time gap calculations.