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arXiv · 2107.01055

Atomic structure optimization with machine-learning enabled interpolation between chemical elements

Sami Kaappa·Casper Larsen·Karsten Wedel Jacobsen
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Abstract

We introduce a computational method for global optimization of structure and ordering in atomic systems. The method relies on interpolation between chemical elements, which is incorporated in a machine learning structural fingerprint. The method is based on Bayesian optimization with Gaussian processes and is applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with CO adsorption, and Cu-Ni clusters. The method consistently identifies low-energy structures, which are likely to be the global minima of the energy. For the investigated systems with 23-66 atoms, the number of required energy and force calculations is in the range 3-75.

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Sami Kaappa, Casper Larsen, Karsten Wedel Jacobsen. 2021-07-02. Atomic structure optimization with machine-learning enabled interpolation between chemical elements. https://doi.org/10.1103/physrevlett.127.166001

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