The search space for the Nb alloy composition is vast, and the ML models have limited utility if they don't also incorporate uncertainty and a measure of expected improvement. As the ML model is built using a limited number of training data, and the candidate selection using the model might be limited to local search. Therefore, we surmised that Bayesian optimization would likely give a better result as this optimization technique considers the prediction uncertainty and balances between local and global search. Bayesian optimization works on a surrogate model and evaluates a utility function . The utility function uses the mean and standard deviation of the candidates estimated by the surrogate model. The utility function encodes a trade-off between the exploitation (candidate searching at points with high mean) and exploration (candidate searching at points with high uncertainty). In this study, we have used GBR as the surrogate model and expected improvement (EI) as a utility function. The utility function is defined as 𝐸𝐼(𝑥) = 𝜎(𝑥)[𝑧Φ(𝑧) + 𝜙(z)], where 𝐸𝐼(𝑥) represents the expected improvement value for each alloy candidate, 𝑧 = [𝜇(𝑥) -𝑓(𝑥 ( ) -𝜀] 𝜎(𝑥) ⁄ , 𝜇 𝑎𝑛𝑑 𝜎 are the predicted output and standard deviation of the candidates obtained from the surrogate model, 𝑓(𝑥 ( ) is the maximum value of the target material property observed in the training data set. Φ represents the cumulative distribution function and 𝜙 is the probability distribution function assuming the target property values follows the normal distribution. The term 𝜀 regulates the amount of exploration, higher the value of 𝜀 more is the exploration. In this method, the largest EI value represents the most promising candidate. We employed Bayesian algorithm and evaluated the EI value for all quaternary/quinary alloy compositions at room temperature (24 degrees centigrade).