Recently, machine learning has been extensively applied to alloy design , such as in the design of high entropy alloys, titanium alloys, copper alloys, shape memory alloys, and bulk metallic glasses. Zhang et al. trained various machine learning classification models using 550 different compositions of high entropy alloys (HEAs) and achieved prediction accuracy more than 90 % while using Neural Network Classifier (Nnet) for identifying different phases (fcc, bcc, or dual-phase) present in the HEAs. For the development of copper alloys with targeted properties such as ultimate tensile strength and electrical conductivity, propertyoriented machine learning-based design strategies have been applied . In a similar approach, Nazarahari et al. used a multilayer feed-forward neural network to design the optimum composition of Ni-Ti superalloy for dental application . The optimum composition of 51.5 at.% Ni with balance Ti showed the lowest amount of Nickel ion release into the dental cavity. Recently, Cheng Wen et al. have successfully applied Bayesian optimization based active learning in AlCoCrCuFeNi high entropy alloy system and discovered new alloy compositions having hardness value 10% higher than the best value in their training dataset.