pyGACE: Combining the genetic algorithm and cluster expansion methods to predict the ground-state structure of systems containing point defects

Searching the most stable atomic-structure of a solid with point defects (including the extrinsic alloying/doping elements), is one of the central issues in materials science. Both adequate sampling of the configuration space and the accurate energy evaluation at relatively low cost are demanding for the structure prediction. In this work, we have employed a framework combining genetic algorithm, cluster expansion (CE) method and first-principles calculations, which can effectively locate the ground-state or meta-stable states of the relatively large/complex systems. We employ this framework to search the stable structures of two distinct systems, i.e., oxygen-vacancy-containing HfO_2−x and the Nb-doped SrTi_1−xNb_xO₃, and more stable structures are found compared with the structures available in the literature. The present framework can be applied to the ground-state search of extensive alloyed/doped materials, which is particularly significant for the design of advanced engineering alloys and semiconductors.