order information on electronic motifs, such as bond delocalization, and the electronic richness or depletion of a bond. Being a density-driven description, QTAIM is built on either theoretical calculations or x-ray diffraction information, and thus is applicable across computational and experimental disciplines. . Exemplar studies functionalize QTAIM to understand ligand-receptor interactions in biological systems , predict chemical activation barriers , describe toxicity , and estimate spectroscopic parameters in organic compounds. Table shows a representative set of descriptors alongside previous interpretations for properties they report on. Given QTAIMs high descriptiveness and prior use in QSAR approaches, we believe that it can be leveraged to improve machine-learned predictions of molecular, protein, and periodic system properties. QTAIM's unique bond definitions, rooted in quantum chemical information, can also serve as powerful alternatives to cheminformatic heuristics such as bond cutoffs for resolving bonding in difficult chemistries involving metals, multicenter bonds, and aromaticity.