Developing new materials has historically been time-consuming. One commonly used approach is materialdoping, in which given a base material, one can change its properties by substituting some elements with new ones or addingadditional elements. Computational material discovery involves searching in a large design space to identify candidates forexperimental verification. Recently, it was possible to obtain many electrical and physical properties of materials by densityfunctional theory based first-principle calculation, making it suitable for computational doping-based material discovery. Incomputational doping, one can substitute some of the atoms in a supercell with dopant atoms. However, the actual positionsof the dopant elements within the supercell are not known. In this work, we developed a genetic algorithm for finding themost 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 fora supercell grid of 75 atoms.We did computational doping on SrTiO$_3$ perovskite. Experiments showed that our method canreduce the running time for computational doping by up to 70% compared with exhaustive sampling as commonly usednow.
Volume 43, 2020
Continuous Article Publishing mode
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