Genetic Algorithm

Genetic algorithms (GA) have proven a good alternative to Monte Carlo type optimization methods for global structure and materials properties optimization. A GA has recently been implemented into ase.

The use of the GA is best learned through tutorials:

• Optimization with a Genetic Algorithm
• Determination of convex hull with a genetic algorithm
• Genetic algorithm Search for stable FCC alloys
• Genetic algorithm search for bulk crystal structures
• Genetic algorithm search for molecular crystal structures

The GA implementation is diverse. It (or previous versions of it) has been used in publications with differing subjects such as structure of gold clusters on surfaces, composition of alloy nanoparticles, ammonia storage in mixed metal ammines and more. The implementation is structured such that it can be tailored to the specific problem investigated and to the computational resources available (single computer or a large computer cluster).

The method is described in detail in the following publications:

For small clusters on/in support material in:

L. B. Vilhelmsen and B. Hammer

A genetic algorithm for first principles global structure optimization of supported nano structures

The Journal of chemical physics, Vol. 141 (2014), 044711

For medium sized alloy clusters in:

S. Lysgaard, D. D. Landis, T. Bligaard and T. Vegge

Genetic Algorithm Procreation Operators for Alloy Nanoparticle Catalysts

Topics in Catalysis, Vol 57, No. 1-4, pp. 33-39, (2014)

A search for mixed metal ammines for ammonia storage have been performed using the GA in:

P. B. Jensen, S. Lysgaard, U. J. Quaade and T. Vegge

Designing Mixed Metal Halide Ammines for Ammonia Storage Using Density Functional Theory and Genetic Algorithms

Physical Chemistry Chemical Physics, Vol 16, No. 36, pp. 19732-19740, (2014)

A simple tutorial explaining how to set up a database and perform a similar search can be found here: Genetic algorithm Search for stable FCC alloys