Searching for alloy configurations with target physical properties: Impurity design via a genetic algorithm inverse band structure approach

The ability to artificially grow different configurations of semiconductor alloys-random structures, spontaneously ordered and layered superlattices-raises the issue of how different alloy configurations may lead to new and different alloy physical properties. We address this question in the context of nitrogen impurities in GaP, which form deep levels in the gap whose energy and optical absorption sensitively depend on configuration. We use the inverse band structure approach in which we first specify a desired target physical property (such as the deepest nitrogen level, or lowest strain configuration), and then we search, via genetic algorithm, for the alloy atomic configurations that have this property. We discover the essential structural motifs leading to such target properties. This strategy opens the way to efficient alloy design.