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

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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.
Original languageEnglish
Article number214101
JournalPhysical Review B
Volume107
Issue number21
Number of pages7
ISSN2469-9950
DOIs
Publication statusPublished - 2023

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