unprecedented progress of the semiconductor industry and information technology. Yet, we have reached a stage where a simple evolution along established research lines might no longer bear much fruit. Advanced functional materials require increasingly complex and demanding property combinations. Their optimization would thus benefit from novel concepts. In thermoelectrics, which convert waste heat into electricity, for example, materials must show the unusual combination of high electrical and small thermal conductivity. This is demanding since a high electrical conductivity is usually accompanied by a high thermal conductivity. In phase change materials (PCMs) employed for data storage and processing, materials are required which possess a pronounced contrast in optical and/or electrical properties between two different states. Usually one of these states is a metastable one, which is typically amorphous, while the second state is then stable crystalline. The metastable state has to be stable at room temperature and slightly above for 10 years; but it should crystallize, i.e., return to the stable crystalline state in a few nanoseconds if heated to temperatures of typically around 500 °C. The combination of pronounced property contrast and hence presumably different atomic arrangements in the two different phases, yet rapid crystallization is indicative for an unusual correlation of chemical bonding, atomic arrangement, and resulting properties, including crystallization kinetics. Topological insulators, expected to help realize novel electronic functionalities, possess topologically protected spin-polarized surface states with high mobility. These states should govern the sample conductivity, if the bulk is insulating.This raises the question how these demanding requirements can be met and how superior materials can be identified. A number of different approaches have been developed in the past two decades to meet these needs. Combinatorial material synthesis, i.e., the fast preparation of stoichiometric libraries and their efficient analysis to identify superior compounds, has already been promoted over two decades ago. [1,2] While this scheme has indeed been successful in improving certain materials such as metal hydrides for hydrogen storage [3] and benchmarking electrocatalysts for solar water splitting, [4] for many material classes still empirical optimization schemes are employed. Machine learning is an emerging strategy to identify materials with a unique property portfolio. [5][6][7] This novel A unified picture of different application areas for incipient metals is presented. This unconventional material class includes several main-group chalcogenides, such as GeTe, PbTe, Sb 2 Te 3 , Bi 2 Se 3 , AgSbTe 2 and Ge 2 Sb 2 Te 5 . These compounds and related materials show a unique portfolio of physical properties. A novel map is discussed, which helps to explain these properties and separates the different fundamental bonding mechanisms (e.g., ionic, metallic, and covalent). The map also provides evidence ...