A schematic workflow for the machine learning guided alloy design strategy has been provided in Fig. . First, we carried out the featurization to represent each alloy in a numerical vector format. We used domain knowledge-based customized material descriptors to create a high dimensional input feature space (details in section: Data collection and featurization). Then, a gradient boost regressor model was trained to predict the targeted property by learning the alloys' feature-property relationship. A list of virtual quaternary and quinary alloy compositions was created. The YS, UTS, and uncertainty of each virtual candidate were calculated with uncertainty calculations resulting from the machine learning process. Thereafter, a Bayesian optimization algorithm was applied to evaluate a utility function based on the uncertainties of prediction for each candidate to suggest suitable alloy composition.