We use a series of revised autocorrelations (RACs) as descriptors for all our machine learning (ML) models as prior work demonstrated RACs to be predictive features for transition metal complex (TMC) properties such as ionization potential, electron affinity, redox potential, and spin-splitting energy. Autocorrelations (ACs) are compact descriptors with d+1 dimensions per physical property encoded at a maximum depth d, which is the maximum bondwise path between atoms. These descriptors depend only on connectivity and do not require structural information such as Cartesian or internal coordinates. The properties we employ are five atom-wise heuristic properties on the 2-dimensional molecular graph: Pauling electronegativity (c); topology (T), which is the coordination number of an atom; nuclear charge (Z); covalent radius (S); and identity (I), which is 1 for any atom. RACs are variants of graph autocorrelations that include both sums of products in standard ACs as well as differences, with distinct scopes from ACs, forming a feature set with metal-centric, coordinating atom-centric, and whole-complex descriptors. A standard AC is evaluated over all atoms in a molecule that are d bonds apart: