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.
Mlip arena: Advancing fairness and transparency in machine learning interatomic potentials via an open, accessible benchmark platform
3 Pith papers cite this work. Polarity classification is still indexing.
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Different uMLIPs encode chemical space in distinct ways, with high cross-model feature reconstruction errors, and fine-tuning preserves strong pre-training bias in the latent features.
Machine learning models trained on quantum mechanical data can predict defect properties in solids with high accuracy but at much lower computational cost than traditional methods.
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Accelerating point defect simulations using data-driven and machine learning approaches
Machine learning models trained on quantum mechanical data can predict defect properties in solids with high accuracy but at much lower computational cost than traditional methods.