Next, we introduce generative ML models to explore the inorganic materials space. Unlike classical methods which require predefined ligands, generative models design novel structures from scratch without any prior knowledge. By learning from realistic data distributions in chemical spaces, generative models can sample numerous, appropriate structures within seconds, which notably accelerates the discovery process. A necessary step for these designed molecules, using either method, is the computational validation of the targeted properties before experimental synthesis. Currently, the main strategy is to use time-consuming DFT calculations. To optimize this process, in Section 3, we explore various ML methods to predict the energy-based properties of TMCs. We also discuss how these classical ML methods can combine with generative models to optimize the design workflow and maximize the efficiency of AI/ML-assisted discovery of new materials. The success of ML relies heavily on reference data. Although the chemical space of TMCs is less explored than that of organic molecules, significant progress has been made to cover TMC chemical space. In Section 4, we list some publicly available data sets in terms of complexes and ligands. These data sets are valuable resources for ML studies in transition metal chemistry.