ML can circumvent human biases and preconceived notions about molecules and materials, thus reanimating the understanding of researchers about chemistry and motivating them to expand the search space for new molecules and materials. However, the inspiration obtained from these ML models is very limited due to the lack of interpretability which exists in all ML models. Although ML methods provide good solutions to various chemical problems, the inference behind these predictions is not transparent, making it difficult to understand how specific molecular features influence the predicted properties or activities. In addition, in some cases such as reaction pathways, the dynamic transition between different states is crucial, however, ML can only give the final state without any intermediate information provided. This hampers a clear understanding of reaction mechanisms and quantum mechanical methods, to some extent, alleviate this dilemma, but these electronic structure methods do not have the same level of accuracy for transition metals as they do for organic molecules. The insights on TMCs are very limited due to the complexity of structures and large chemical space for TM complexes also impedes the utilization of DFT methods in transition metal chemistry because of the computational cost. The lack of fully validated electronic structure methods also influences the quality of reference data, thus misleading the fitting of ML models. Finally, for practical applications, only computational validation on designed molecules is not enough because there still exists a fundamental gap between theory and experiments. 166 Whether these computationally validated molecules really have targeted properties is still an open question and need further experimental analysis. The computational design is certainly not the endpoint. Rather, these design tools should be used by experimentalists to explore TMC chemical space to help in precisely locating targets for synthesis and evaluation.