Geometry-diverse fine-tuning, not single-geometry adaptation, best restores accuracy of foundation interatomic potentials on ZrO2 nanostructures.
This result is consistent with the strong representation of bulk crystalline environments in the pretraining data
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.mtrl-sci 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires
Geometry-diverse fine-tuning, not single-geometry adaptation, best restores accuracy of foundation interatomic potentials on ZrO2 nanostructures.