One of the simplest approaches for featurization in materials informatics is to do a one-hot encoding based on the atom or weight fraction of the alloying elements to construct a composition-based feature vector (CBFV). For example, Jha et al. use only the mole fraction of each element as input features and successfully implement a deep neural network model known as ElemNet to predict the formation enthalpy of materials. In this case, the ElemNet model was trained with a few thousand Open Quantum Materials Database (OQMD) samples . While this approach can work well with large data sets, in the case of a limited dataset (e.g., only 18 unique alloys), ML models will likely fail to capture the underlying chemistry between the alloying elements and the property. Accordingly, the trained model will also be unlikely to perform well while screening the optimum alloy composition from an unexplored design space with unseen alloying elements that are not present in the training dataset. To help overcome this dearth of data, in this study, we constructed a CBFV that used elemental descriptors such as Oliynyk elemental descriptor and other custom domain knowledge-based descriptors.