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.
Mlip arena: Advancing fairness and transparency in machine learning interatomic potentials via an open, accessible benchmark platform
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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Comparing the latent features of universal machine-learning interatomic potentials
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.
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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.