In 2021, Ang and coworkers built a SchNet RMLP for a difficult trispericyclic reaction leading to one [8+2] and two [6+4] cycloaddition products (Figure ) . To describe highly complex bifurcation PES, they combined Nudged-Elastic Band (NEB) and reactive MD with NNP during active learning iterations to ensure comprehensive coverage of PES with a focus on reaction pathways. They further conducted transfer learning to higher-level QM data with an implicit solvation model to study how solvent affects product selectivity through reactive MD with RMLPs. In 2022, Young et al. built a data-efficient ACE RMLP on cycloaddition between tropone and cycloheptatriene with an ambimodal TS leading to three cycloadducts . In 2023, Zhang et al. applied a quasi-classical trajectory sampling to build a DeepPot-SE RMLP for the dimerization of cyclopentadiene, a PTSB reaction with two cycloadducts . Quasi-classical trajectory sampling allows coverage around the MEP rather than strictly following the MEP. They achieved 1 meV/atom RMSE without doing active learning. Building accurate RMLPs for these bifurcating reactions allows efficient reactive MD simulations to study products distributions and reaction trajectory analysis which are not feasible with simple TST or would require very expensive AIMD.