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Genetic Algorithm for Grain Boundary and Crystal Structure Predictions

We developed a global structure optimization method, genetic algorithm, for an efficient prediction of grain- boundary structures. Using this method, we predicted the most stable structures and a number of low-energy metastable structures for Si[001] symmetric tilted grain boundaries with various tilted angles. We show that most of the grain-boundary structures can be described by the structural unit model with the units being the dislocation cores and perfect-crystal fragments (see Fig. 1). The energies of the grain boundary structures obtained from the genetic algorithm optimization are evaluated by tight-binding calculations using the environment-dependent Si tight-binding potential and found to be in very good agreement with the first-principles calculation results. We also combine the efficient algorithms (e.g., genetic algorithm and basin hopping method) for exploring configuration space with accurate ab initio calculations for energy evaluations to predict the structures of complex crystals and binary alloys from first-principles. The search successfully predicted the complex crystal structure of boron at high pressure with 28 atoms per unit cell.& The first-principles GA search also successfully predict the ground state structures of AL2Sc6 and Al4Sc4 binary alloys (see Fig. 2).

Highlight Date: 
Saturday, April 11, 2009
Article Title: 

Finding the low-energy structures of Si[001] symmetric tilted grain boundaries with a genetic algorithm

Jian Zhang, Cai-Zhuang Wang, and Kai-Ming Ho
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Journal Name: 
Phys. Rev. B
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