Ligand denticity influences organometallics in various ways, such as the stability of complexes, 124,125 the geometry of complexes, 126,127 and the reactivity of complexes. Although generative models have paved the way for the design of molecules with tailored properties, AI-assisted methods still face challenges. Currently, almost all candidates designed by generative models are computationally validated for targeted properties and the evaluation of properties is performed by checking the energetics of candidates, such as the bonding affinities, the interaction energies and the free energies, etc. Such computation heavily relies on quantum mechanical methods, which however, are time-consuming and resource intensive. To further accelerate the discovery of chemical space, efficient strategies for property evaluations should be proposed. One possible approach is to combine generative models with machine learning potentials (MLPs). The latter is a family of ML methods which model the potential energy surface of chemical systems of interest. A well-trained model is able to predict the energetics with several orders of magnitude faster than traditional DFT methods. Moreover, NNPs designed for TMCs should be able to model manifold interactions, including the metal-ligand interactions, the ligand-ligand interactions, or even metal-metal interactions, which requires the architectures to be highly versatile. In addition, since almost any molecular species which can donate electrons can be a ligand, the diverse ligand types also increase the complexity of TMCs, and thus poses outstanding challenges to the generality of NNPs designed for TMCs.