Nine universal machine-learning potentials were run in unconstrained evolutionary searches for the ground states of twelve inorganic compounds; performance ranges from near-DFT accuracy (eSEN) to essentially non-predictive (M3GNet), and the searches produced two new predicted phases.
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Performance of universal machine learning potentials in global optimization of inorganic crystal structures
Nine universal machine-learning potentials were run in unconstrained evolutionary searches for the ground states of twelve inorganic compounds; performance ranges from near-DFT accuracy (eSEN) to essentially non-predictive (M3GNet), and the searches produced two new predicted phases.