Evolutionary search for new high-kdielectric materials: methodology and applications to hafnia-based oxides

Abstract

High-kdielectric materials are important as gate oxides in microelectronics and as potential dielectrics for capacitors. In order to enable computational discovery of novel high-kdielectric materials, we propose a fitness model (energy storage density) that includes the dielectric constant, bandgap, and intrinsic breakdown field. This model, used as a fitness function in conjunction with first-principles calculations and the global optimization evolutionary algorithm USPEX, efficiently leads to practically important results. We found a number of high-fitness structures of SiO2and HfO2, some of which correspond to known phases and some of which are new. The results allow us to propose characteristics (genes) common to high-fitness structures – these are the coordination polyhedra and their degree of distortion. Our variable-composition searches in the HfO2–SiO2system uncovered several high-fitness states. This hybrid algorithm opens up a new avenue for discovering novel high-kdielectrics with both fixed and variable compositions, and will speed up the process of materials discovery.