A grand canonical global optimization algorithm with on-the-fly trained Gaussian process potentials finds stable structures and stoichiometries of clusters and surfaces using fewer first-principles evaluations.
Wang , author J.-X
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Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
A grand canonical global optimization algorithm with on-the-fly trained Gaussian process potentials finds stable structures and stoichiometries of clusters and surfaces using fewer first-principles evaluations.