Developing new materials has historically been time-consuming. One commonly used approach is material doping, in which given a base material, one can change its properties by substituting some elements with new ones or adding additional elements. Computational material discovery involves searching in a large design space to identify candidates for experimental verification. Recently, it was possible to obtain many electrical and physical properties of materials by density functional theory based first-principle calculation, making it suitable for computational doping-based material discovery. In computational doping, one can substitute some of the atoms in a supercell with dopant atoms. However, the actual positions of the dopant elements within the supercell are not known. In this work, we developed a genetic algorithm for finding the most stable structure of the doped material with the lowest free electronic energy. For each candidate atom configuration, we use the Vienna Ab-Initio Simulation Package to calculate its physicochemical properties, which takes about 15-30 h for a supercell grid of 75 atoms. We did computational doping on SrTiO 3 perovskite. Experiments showed that our method can reduce the running time for computational doping by up to 70% compared with exhaustive sampling as commonly used now.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.