As previously mentioned, reactions in multiple reactant systems are far more complex compared to single reaction systems, which consequently leads to a different logic in model development. First, the complexity of multiple reaction systems requires not only the approximating ability of the ML model but also the quality, or in other words, the diversity of the dataset the model is trained on. The training set must cover a wide range of different species, sub-processes, and reaction types in order to provide the model with enough information on all possible reactions that could occur in a multiple reaction system. Emerging RMLPs that focus on modeling multiple reaction systems often require extensive reactive data sampling strategies like AL, evolutionary algorithms, or the usage of transfer learning. This, to some extent, can be regarded as a special method of data augmentation. Second, although the accuracy of the PES prediction in multiple reaction systems is still critical, RMLPs on multiple reaction systems are more often validated by examining whether they can reproduce some known physical or chemical processes on a macro scale, such as the reaction rate, the ratio between product species, or the predicted crystal structures.