Output variants¶
Models can provide multiple variants of the same output, for example different exchange–correlation functionals for the energy. Users of a model can then select which variant of the output should be used in simulation engines and workflows. Variants are also sometimes referred to as heads, especially in the context of deep learning models.
Variants are identified by appending "/<variant>" to the base output name.
For example:
energy(default)energy/pbeenergy/pbe0energy/r2scan
Important
If a model defines one or more variants, it must also define the default
base output (e.g. energy). Both the default and its variants follow the
same output metadata rules.
The following simulation engines can use variants for all their supported outputs:
