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:
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. HammerThe Journal of chemical physics, Vol. 141 (2014), 044711
For medium sized alloy clusters in:
S. Lysgaard, D. D. Landis, T. Bligaard and T. VeggeTopics 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. VeggeDesigning Mixed Metal Halide Ammines for Ammonia Storage Using Density Functional Theory and Genetic AlgorithmsPhysical 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: GA Search for stable FCC alloys