Herein, we focus on three papers on exploring photodynamic mechanisms conducted by Li and coworkers . They combined NN with NAMD in their P yRAI 2 M D package to study photodynamics in three reactions. They adopted Wigner sampling/geometrical interpolation and active learning for automatically generating data on the most relevant S 0 , S 1 states and especially the crossing conical intersection region. With trained RMLPs, they first conducted long-lived NAMD simulations to a 4π disrotatory electrocyclic ring-closing reaction of norbornyl cyclohexadiene (NCHD) and rationalize the stereoselectivity in major (anti-NL) and minor (syn-NL) products . They successfully predicted the existence of the minor syn-product, which is missing in short time QM-NAMD trajectories. Similarly, they studied the substituent effect on [2+2] cycloaddition of 3-ladderdiene to form cubane (Figure ). The RMLP-NAMD trajectories discovered four S 1 relaxation pathways. They found replacing the methyl(CH 3 ) substitution to trifluoromethyl (CF 3 ) or cyclopropyl (cPr) groups can significantly increase the yield of cubane from 1% to 14% and 15% . Furthermore, they applied RMLP-NAMD in another 4π disrotatory electrocyclization from hexafluorobenzene(HFB) to form strained Dewar-HFB, where the hexafluorination of benzene significantly enhances the chemoselectivity (Figure ) . The efficient RMLP-NAMD is able to discover the detailed mechanisms of the long-lived nonradiative decay of HFB, which is not even feasible with QM methods. All of these elegant results show good examples of how to apply ML to accelerate expensive NAMD simulations.