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Description: Machine-learned interatomic potentials (ML-IAP) that have been trained on large, chemically and structural diverse datasets. For materials, datasets that include a majority of the periodic table.
Category Subtitle:
Additional context:
Since ca. 2023, universal potentials start to appear. Some have already beend added here. Initial examples include M3GNet, CHGNet, MACE-MP0 and others.
Care should be taken to sharpen the definition.
Possible overlaps, future conflicts with related concepts. Foundation models, multi-modal and multi-target learning models and frameworks.
The text was updated successfully, but these errors were encountered:
Category details:
uip
Additional context:
Since ca. 2023, universal potentials start to appear. Some have already beend added here. Initial examples include M3GNet, CHGNet, MACE-MP0 and others.
Care should be taken to sharpen the definition.
Possible overlaps, future conflicts with related concepts. Foundation models, multi-modal and multi-target learning models and frameworks.
The text was updated successfully, but these errors were encountered: