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A budget-dependent crossover between coverage- and response-based training-set selection for machine-learned interatomic potentials
Selecting compact training sets for machine-learned interatomic potentials requires deciding whether to preserve structural diversity or target configurations on which models disagree. The better choice can depend on how much data is retained, making a comparison at one training-set size insufficien
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