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

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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Cited by in corpus (8)

• GPAW: An open Python package for electronic-structure calculations
• Global optimization of atomistic structure enhanced by machine learning
• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
• Generating candidates in global optimization algorithms using complementary energy landscapes
• Machine-learning enabled optimization of atomic structures using atoms with fractional existence
• Accelerating structure search using atomistic graph-based classifiers
• Atomistic Global Optimization X: A Python package for optimization of atomistic structures
• Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence