We tried different regression models such as GBR, RF, SVR, and KNN with different featurization schemes to predict the targeted mechanical properties. GBR performed better for YS and UTS in the training and test datasets (Fig. and Table -S2, SI). We have observed that when we used domain knowledge based custom features, the accuracy of prediction for most of the ML models have been improved for the test dataset. GBR with custom feature has the highest R 2 score of 0.84 and the lowest MAE value of 57.42 MPa for predicting UTS. Similarly, GBR with custom feature has the highest R 2 score of 0.77 and the lowest MAE value of 73.427 MPa for predicting YS. With this result, we emphasize that domain knowledge-based feature selection is very effective when we have limited datasets. Actual vs. predicted strength values of GBR with custom features are shown in Fig. . We observed that prediction accuracy at a higher strength value is low because of the data sparsity in the higher strength regime. As GBR outperforms the rest of the models, we continued with GBR as a surrogate model for Bayesian optimization. We used the bootstrap method to measure the prediction uncertainty (Fig. ). We estimated the mean and standard deviation of the target properties for each alloy composition created in the virtual candidate space. The estimated mean and standard deviation were used in the Bayesian optimization to screen out optimal alloy compositions. Mean, standard deviation, and expected improvement values for the first seven suggested alloy compositions are presented in the supplementary information (Table -S5, SI).