Place: Big-Data driven Materials Science Workshop, Lausanne, Switzerland
The prediction of the crystal structure of a material from only its chemical composition is one of the key challenges in materials design. We recently used a cluster analysis of experimentally observed crystal structures of binary p-d valent compounds to derive a 3D structure map that is systematically optimised to reach high predictive power . The three descriptors of the structure map are physically intuitive functions of the number of valence electrons, the atomic volume and the electronegativity of the constituent elements. We demonstrate that these descriptors are transferable to binary and ternary p-d compounds with off-stoichiometric compositions and to ternary prototypes . We demonstrate the application of the structure map, particularly for the prediction of alloying windows that preserve a given prototype and alloying routes from a given composition to a desired prototype.