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Modelling and understanding properties of materials from first principlesrequire knowledge of the underlying atomistic structure. This entails knowingthe individual identity and position of all involved atoms. Obtaining suchinformation for macro-molecules, nano-particles, clusters, and for the surface,interface, and bulk phases of amorphous and solid materials represents adifficult high dimensional global optimization problem. The rise of machinelearning techniques in materials science has, however, led to many compellingdevelopments that may speed up such structure searches. The complexity of thenew methods have established the necessity for an efficient way ofexperimenting with and assembling them into global optimization algorithms. Inthis paper we introduce the Atomistic Global Optimization X (AGOX) frameworkand code, as a customizable approach to building efficient global optimizationalgorithms. A modular way of expressing global optimization algorithms isdescribed and modern programming practices are used to enable that modularityin the freely available AGOX python package. Two examples of globaloptimization problems are analyzed: One that is computationally inexpensivewhich is used to showcase that AGOX enables the expression of multiple globaloptimization algorithms. As the other example, AGOX is used for solving acomplex atomistic optimization problem for a metal-nitride nano-clusterembedded in a graphene sheet as described at the density functional theory(DFT) level.

Atomistic Global Optimization X: A Python package for optimization of atomistic structures