In addition to integrating RMLP within QM/MM schemes, one could remove all couplings and directly train on the whole system, including solvent. The main difficulties come from properly sampling solvent molecules around the reaction center and the computational expense of performing QM calculations on large systems. In 2019, the Yang group developed a TensorMol NNP for 1,3-dipolar cycloaddition reaction in explicit water solvents to study the role of water in the reaction . They collected a compact but comprehensive dataset to cover water-water, water-reactant and reactant-reactant interactions respectively. Through reactive MD with RMLP, they observed an interesting stepwise mechanism with methyl vinyl ketone (MVK) as dipolarophile in MD trajectories, which cannot be observed with an implicit solvation model since water significantly stabilizes the stepwise intermediate through a strong hydrogen bonding. In 2022, the Parrinello group applied DeepMD to study the complicated PES of urea decomposition in explicit water solution . To properly sample on the complicated global PES, they ran AIMD well-tempered metadynamics (WTMetaD) to capture rare events for a solvated urea system (1 urea surrounded by 34 waters), followed by active learning (AL)