The concept of computational materials design envisions the identification of new synthetically-accessible structures with desirable properties and the optimization of known systems using first-principles calculations. While significant steps towards realizing this vision have been made, notably in atomistic property evaluation, the computational prediction of materials synthesis and realistic structure remains a challenge. Dr. Kitchaev will describe my work towards resolving these obstacles using three examples where models constructed from first-principles data yield quantitative synthesis predictions, describe the structure of the resulting materials, and reveal design criteria for optimizing materials behavior. He will first show that the synthesis routes used to obtain the diverse array of crystal structures observed in the manganese oxides can be quantitatively described with a quasi-equilibrium view of crystallization which takes into account the effects of off-stoichiometry, finite-size effects and hydration. He will then apply a similar synthesis model to the design and optimization of disordered rocksalt oxyfluoride Li-ion battery cathode materials where electrochemical behavior is strongly coupled to chemical short range order. Finally, he will describe the computational design of materials capable of hosting magnetic skyrmion phases, which are nanoscale magnetic textures with nontrivial topology, focusing on ensuring that these phases are thermally robust and tunable as is necessary for spintronic device applications. In all three cases, experimental tests validate the predictive power of the computational analysis. While numerous open questions remain to be resolved before generally predictive synthesis-aware materials design is possible, these initial results demonstrate the transformative potential of these tools to the development of new functional materials.