My work involves the computational designing of the alloys for additive manufacturing. Different applications have different target properties and need a well optimized target specific material. For example, certain applications may require easy printability, while other may need high-temperature strength. The properties of a material depend on many factors such as thermal conductivity, carbon content, corrosion resistance, thermal expansion, temperature difference between the solidus and liquidus, number of phases present in the system, etc. To effectively model or design a new material for the target property, we use cooperative CALPHAD, electronic structure, and machine learning-based models. Typically, we perform various thermodynamic and electronic structure calculations to gather information related to its mechanical, thermodynamic, and electronic properties. Then we apply an appropriate machine learning model to capture the relationship among various features like elemental composition of the alloy, its phase stability, and the target properties to design the target specific new material.