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\section{Materials Design Workflow}  \label{sec:workflow}  In materials design, our playground is the combinatorial phase space of all possible materials. Attempts to search for novel compositions and predict stable structures in this huge space by brute force computation would require unreasonble computational resources and timescales. In practice, we begin with an intuitive idea of what classes of materials to explore, and how chemistry would enhance desireable solid state properties. Restricting ourselves to a few materials classes renders the search space tractable. In areas with firm theoretical understanding, we perform quantitative calculations with established computational tools which can confirm or refute our intuition, and the process iterates. Occasionally, we find that no material matches our design criteria within our restricted materials classes and we need to expand the search space. In classes where the state of theory is less developed, namely strongly correlated materials, we can appeal to analogies, descriptors and pattern recognition to help guide the design process~\cite{Norman_2016}. In the following, we describe the three steps in our workflow for materials design and note the role correlations play in each step.  %%  The search for new materials starts with some intuitive idea of what classes of materials should be explored, and how certain chemistry enhances desireable solid state observables. In areas where there is good theoretical understanding and working computational tools, this intuition can then be supplemented by more quantitative calculations. When this understanding is lacking, one can appeal to analogies, descriptors and machine learning .[ CITE NORMAN'S REVIEW ? arXiv:1601.00709 ]. Designing a proper strategy, thus requires an understanding of the nature of correlations in a given class of materials. Irrespectively of the degree of correlation, it is natural to divide the workflow of materials design into three different steps, of course, how to carry them out in practice will depend on the level of correlation. The first, and most well-studied, is the calculation of the electronic structure. namely how to go from structure to property, i.e. given a crystal structure, compute its electronic properties, such as gap size, magnetic ordering, and superconducting transition temperature. Here, density functional theory, has been quite successful for weakly correlated systems. Sometimes, like in the case of semiconducting materials, where the standard implementations of DFT considerably underestimates the effects of exchange at low enerties, the first correction in the screened Coulomb interactions, namely the GW self energy, $\Sigma_{GW} - {V^{LDA}}_{XC}$ is required to get good results.