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

Understand

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.