A1 Journal article – refereed
Optimizing atomic structures through geno-mathematical programming

List of Authors: Lahti A., Östermark, Kokko K.
Publication year: 2019
Journal: Communications in Computational Physics
Volume number: 25
Issue number: 3
eISSN: 1991-7120


In this paper, we describe our initiative to utilize a modern well-tested numerical

platform in the field of material physics: the Genetic Hybrid Algorithm (GHA).

Our aim is to develop a powerful special-purpose tool for finding ground state structures.

Our task is to find the diamond bulk atomic structure of a silicon supercell

through optimization. We are using the semi-empirical Tersoff potential. We focus on

a 2x2x1 supercell of cubic silicon unit cells; of the 32 atoms present, we have fixed 12

atoms at their correct positions, leaving 20 atoms for optimization. We have been able

to find the known global minimum of the system in different 19-, 43- and 60-parameter

cases. We compare the results obtained with our algorithm to traditional methods of

steepest descent, simulated annealing and basin hopping. The difficulties of the optimization

task arise from the local minimum dense energy landscape of materials and

a large amount of parameters. We need to navigate our way efficiently through these

minima without being stuck in some unfavorable area of the parameter space. We

employ different techniques and optimization algorithms to do this.

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Last updated on 2019-13-03 at 08:59