This package provides a general interface to families of
machine-learning interatomic potentials (MLIAPs). This interface consists
of a mliap pair style and a mliap compute.
The mliap pair style is used when running simulations with energies and
forces calculated by an MLIAP. The interface allows separate
definitions of the interatomic potential functional form (*model*)
and the geometric quantities that characterize the atomic positions
(*descriptor*). By defining *model* and *descriptor* separately,
it is possible to use many different models with a given descriptor,
or many different descriptors with a given model. Currently, the pair_style
supports just two models, *linear* and *quadratic*,
and one descriptor, *sna*, the SNAP descriptor, including the
linear, quadratic, and chem variants. Work is currently underway to extend
the interface to handle neural network energy models,
and it is also straightforward to add new descriptor styles.
The mliap compute style provides gradients of the energy, force,
and stress tensor w.r.t. model parameters.
These are useful when training MLIAPs to match target data.
Any *model or *descriptor* that has been implemented for the
*mliap* pair style can also be accessed by the *mliap* compute.
In addition to the energy, force, and stress gradients, w.r.t.
each *model* parameter, the compute also calculates the energy,
force, and stress contributions from a user-specified
reference potential.
To see how this command
can be used within a Python workflow to train machine-learning interatomic
potentials, see the examples in FitSNAP https://github.com/FitSNAP/FitSNAP>.
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