Compared to supported monometallic oxides, mixed oxides are highly versatile in terms of their compositional, structural, redox, and surface properties. As such, they offer exciting possibilities for various chemical looping related applications. The challenge, however, resides in the difficulty in efficiently identifying suitable oxide compositions given the vast material design space. This presentation discusses the use of high throughput computation and data science tools to rationally design mixed metal oxides for chemical looping beyond combustion. Specific applications include chemical looping air separation, redox based CO2 and water splitting, and redox-activated sorbents for green hydrogen generation. Specifically, the screening criteria in each case are first developed through thermodynamic principles. This is followed by high throughput density functional theory (DFT) calculations covering large families of mixed oxides and extension to even larger material families using machine learning tools. The materials identified by computational screening are subsequently verified experimentally, confirming the effectiveness of the proposed approach. Finally, descriptors that correlate with the rates of the chemical looping reactions are also explored.