Machine-learning enabled optimization of atomic structures using atoms with fractional existence

Abstract

We introduce a method for global optimization of the structure of atomic systems that uses additional atoms with fractional existence. The method allows for movement of atoms over long distances bypassing energy barriers encountered in the conventional position space. The method is based on Gaussian processes, where the extrapolation to fractional existence is performed with a vectorial fingerprint. The method is applied to clusters and two-dimensional systems, where the fractional existence variables are optimized while keeping the atomic positions fixed on a lattice. Simultaneous optimization of atomic coordinates and existence variables is demonstrated on copper clusters of varying size. The existence variables are shown to speed up the global optimization of large and particularly difficult-to-optimize clusters.

arXiv comment6 pages, 5 figures, plus supplement

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• Accelerating structure search using atomistic graph-based classifiers
• Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence