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.
Lykhach , author S
1 Pith paper cite this work, alongside 669 external citations. Polarity classification is still indexing.
1
Pith paper citing it
669
external citations · OpenAlex
fields
cond-mat.mtrl-sci 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.