Model

Edge Basis

Basic

r_max

Type: float
Default: n/a

The cutoff radius within which an atom is considered a neighbor.

irreps_edge_sh

Type: Irreps or int
Default: n/a

The irreps to use for the spherical harmonic projection of the edges. If an integer, specifies all spherical harmonics up to and including that integer as \(\ell_{\text{max}}\). If provided as explicit irreps, all multiplicities should be 1.

num_basis

Type: int
Default: 8

The number of radial basis functions to use.

chemical_embedding_irreps_out

Type: Irreps
Default: n/a

The size of the linear embedding of the chemistry of an atom.

Advanced

BesselBasis_trainable

Type: bool
Default: True

Whether the Bessel radial basis should be trainable.

basis

Type: type
Default: <class 'nequip.nn.radial_basis.BesselBasis'>

The radial basis to use.

Convolution

Basic

num_layers

Type: int
Default: 3

The number of convolution layers.

feature_irreps_hidden

Type: Irreps
Default: n/a

Specifies the irreps and multiplicities of the hidden features. Typically, include irreps with all \(\ell\) values up to \(\ell_{\text{max}}\) (see irreps_edge_sh), each with both even and odd parity. For example, for irreps_edge_sh: 1, one might provide: feature_irreps_hidden: 16x0e + 16x0o + 16x1e + 16x1o.

Advanced

invariant_layers

Type: int
Default: 1

The number of hidden layers in the radial neural network.

invariant_neurons

Type: int
Default: 8

The width of the hidden layers of the radial neural network.

resnet

Type: bool
Default: False

nonlinearity_type

Type: str
Default: gate

nonlinearity_scalars

Type: dict
Default: {'e': 'ssp', 'o': 'tanh'}

nonlinearity_gates

Type: dict
Default: {'e': 'ssp', 'o': 'abs'}

use_sc

Type: bool
Default: True

Output block

Basic

conv_to_output_hidden_irreps_out

Type: Irreps
Default: n/a

The middle (hidden) irreps of the output block. Should only contain irreps that are contained in the output of the network (0e for potentials).

Advanced