SN2-type reactions represent a fundamental mechanism in organic chemistry and biological processes, which have been widely selected to build RMLPs (Figure ). For example, Brickel et al. compared PhysNet model and two empirical reactive FFs (MS-ARMD, MS-VALBOND) on a simple (Cl -+ H 3 C-Br) SN2 reaction . They found that PhysNet achieves very high accuracy of 0.0025 kcal/mol RMSE with training on 10k data. Recent work by Young et al. and Piskor et al. adopted the data-efficient kernel-based GAP and sGDML methods to similar gas phase SN2 systems and trained to only 55 and 50 data points, achieving high accuracy. A global PES built with FI-NN was created by Lu et al. on a 9 atoms system (F -(H2O) + CH 3 -I) to study the effect of a single water molecule on a SN2 reaction, and a 14 atoms system (F -+ Me 3 C-I) to study the competing SN2 and E2 mechanisms. In addition to specific SN2 reactions, Eckhoff and Reiher group developed lifelong RMLP with element-embracing atom-centered symmetry functions (eeACSFs) to train on 42 methyl carbon centered SN2 reactions with 10 different elements . Given the fact that most SN2 reactions have a formal negative charge, it is important to include the environment (solvent) around the reaction, which will be discussed in a later section.