Starts 14 Mar 2016 11:00
Ends 14 Mar 2016 12:30
Central European Time
Computational materials design is an emerging research paradigm in which state-of-the-art electronic structure methods are applied to predict materials with target properties within families of candidate compounds. In this seminar, as an example of materials design driven by ab initio methods, I will describe the application of density functional theory (DFT), and its corrections and extensions, to predict new p-type transparent conducting materials (TCMs). While TCMs are crucial in optoelectronics, transparency and p-type conductivity rarely coexist in the same material. To identify new candidate p-type TCMs, we screened the family of known Ag and Cu oxides and identified Ag3VO4 and KAg11(VO4)4 as p-type conductors transparent to the red light. In these oxides, the Ag vacancies act as intrinsic hole-generating defects, and the hole effective mass is lighter than in CuAlO2, the prototypical p-type transparent conducting oxide. However, ab initio methods require the crystal structure as input information to predict materials properties. Therefore, the ability to predict the crystal structure of hypothetical compounds without constraints or assumptions on the lattice symmetry is essential in order to discover new functional materials beyond the repertoires of known solids. Here, I will describe a crystal structure prediction method based on an evolutionary algorithm to search for the global total-energy minimum of a solid calculated by DFT as a function of the lattice vectors and atom positions. I will illustrate the application of this global space-group optimization (GSGO) algorithm to selected binary and ternary solids. Finally, I will discuss an extension of the GSGO algorithm in which the stoichiometry is also optimized so as to predict at the same time the composition and crystal structure of the thermodynamically stable phases of a solid system.