Zhang et al. presented a more general RMLP. Instead of sampling conformers from a deterministic reaction system, they employed a more aggressive sampling strategy with the help of the nanoreactor sampler. This involved a simulation box in which artificial disturbances such as temperature changes, shock waves, and compressions were frequently applied to the molecules. Starting with randomly placed small molecules with random concentrations, the ANI-1nr ensemble-based RMLP was then iteratively trained following an AL strategy, using the energy output uncertainty from the ensemble as the selection criteria for new conformers. The successful data sampling strategy attests to the generalizability, which was proven by successfully reproducing multiple types of combustion: methane combustion, biofuel combustion with organic additives, and acetylene oxidation under multiple oxygen concentrations. In particular, none of the reactants in the biofuel system they tested were included in the training set. Moreover, their model also shows good transferability by reproducing the carbon nucleation process, carbon phase diagram, and prebiotic chemistry.