Transition metal complexes play an important role in a wide range of chemistries including catalysis, 1 magnetic materials, 2 and biological systems. Over the past several decades, computational models and methods for transition metals have become increasingly efficient and accurate; meanwhile, computers have also become orders of magnitude faster. All these factors have made computational studies of transition metal chemistry increasingly widespread, reliable, and predictive. While computational studies have become an important driving force in transition metal chemistry, it can be quite difficult to achieve chemical accuracy (error ∼ 1 kcal/mol) using available models due to the complex nature of the electronic structure of transition metal compounds. As a result, routine modeling of transition metal systems often employs Density Functional Theory (DFT). As a single-reference method, DFT treatment of transition metals lacks a description of static correlation energy, often yielding unsatisfying results and limiting the scope of potential applications. A computationally feasible approach that has seen widespread application is brokensymmetry DFT. This scheme employs an unrestricted Kohn-Sham determinant that breaks spin symmetry in the density. Analysis of the broken-symmetry determinant shows that it is a mixture of different spin-pure configurations. Errors associated with this spin contamination can significantly change the energy and distort the potential energy surface (PES).