Finding the reconstructions of semiconductor surfaces via a genetic algorithm

TL;DR

This paper introduces a genetic algorithm-based method to determine semiconductor surface reconstructions, efficiently generating structural candidates for electronic analysis and comparison with experimental STM images.

Contribution

The paper presents a novel genetic algorithm approach combined with optimized interatomic potentials for surface reconstruction prediction.

Findings

Successfully identified known low-energy structures of Si(105)

Generated new structural configurations for semiconductor surfaces

Provided a database of surface reconstructions for further study

Abstract

In this article we show that the reconstructions of semiconductor surfaces can be determined using a genetic procedure. Coupled with highly optimized interatomic potentials, the present approach represents an efficient tool for finding and sorting good structural candidates for further electronic structure calculations and comparison with scanning tunnelling microscope (STM) images. We illustrate the method for the case of Si(105), and build a database of structures that includes the previously found low-energy models, as well as a number of novel configurations.