# SPDX-License-Identifier: LGPL-3.0-or-later
import numpy as np
try:
from deepmd_utils._version import version as __version__
except ImportError:
__version__ = "unknown"
import copy
from typing import (
Any,
List,
Optional,
)
from .common import (
DEFAULT_PRECISION,
NativeOP,
)
from .env_mat import (
EnvMat,
)
from .network import (
EmbeddingNet,
NetworkCollection,
)
[docs]class DescrptSeA(NativeOP):
r"""DeepPot-SE constructed from all information (both angular and radial) of
atomic configurations. The embedding takes the distance between atoms as input.
The descriptor :math:`\mathcal{D}^i \in \mathcal{R}^{M_1 \times M_2}` is given by [1]_
.. math::
\mathcal{D}^i = (\mathcal{G}^i)^T \mathcal{R}^i (\mathcal{R}^i)^T \mathcal{G}^i_<
where :math:`\mathcal{R}^i \in \mathbb{R}^{N \times 4}` is the coordinate
matrix, and each row of :math:`\mathcal{R}^i` can be constructed as follows
.. math::
(\mathcal{R}^i)_j = [
\begin{array}{c}
s(r_{ji}) & \frac{s(r_{ji})x_{ji}}{r_{ji}} & \frac{s(r_{ji})y_{ji}}{r_{ji}} & \frac{s(r_{ji})z_{ji}}{r_{ji}}
\end{array}
]
where :math:`\mathbf{R}_{ji}=\mathbf{R}_j-\mathbf{R}_i = (x_{ji}, y_{ji}, z_{ji})` is
the relative coordinate and :math:`r_{ji}=\lVert \mathbf{R}_{ji} \lVert` is its norm.
The switching function :math:`s(r)` is defined as:
.. math::
s(r)=
\begin{cases}
\frac{1}{r}, & r<r_s \\
\frac{1}{r} \{ {(\frac{r - r_s}{ r_c - r_s})}^3 (-6 {(\frac{r - r_s}{ r_c - r_s})}^2 +15 \frac{r - r_s}{ r_c - r_s} -10) +1 \}, & r_s \leq r<r_c \\
0, & r \geq r_c
\end{cases}
Each row of the embedding matrix :math:`\mathcal{G}^i \in \mathbb{R}^{N \times M_1}` consists of outputs
of a embedding network :math:`\mathcal{N}` of :math:`s(r_{ji})`:
.. math::
(\mathcal{G}^i)_j = \mathcal{N}(s(r_{ji}))
:math:`\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}` takes first :math:`M_2` columns of
:math:`\mathcal{G}^i`. The equation of embedding network :math:`\mathcal{N}` can be found at
:meth:`deepmd.utils.network.embedding_net`.
Parameters
----------
rcut
The cut-off radius :math:`r_c`
rcut_smth
From where the environment matrix should be smoothed :math:`r_s`
sel : list[str]
sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuron : list[int]
Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`
axis_neuron
Number of the axis neuron :math:`M_2` (number of columns of the sub-matrix of the embedding matrix)
resnet_dt
Time-step `dt` in the resnet construction:
y = x + dt * \phi (Wx + b)
trainable
If the weights of embedding net are trainable.
type_one_side
Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
exclude_types : List[List[int]]
The excluded pairs of types which have no interaction with each other.
For example, `[[0, 1]]` means no interaction between type 0 and type 1.
set_davg_zero
Set the shift of embedding net input to zero.
activation_function
The activation function in the embedding net. Supported options are |ACTIVATION_FN|
precision
The precision of the embedding net parameters. Supported options are |PRECISION|
multi_task
If the model has multi fitting nets to train.
spin
The deepspin object.
Limitations
-----------
The currently implementation does not support the following features
1. type_one_side == False
2. exclude_types != []
3. spin is not None
References
----------
.. [1] Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, and E. Weinan. 2018.
End-to-end symmetry preserving inter-atomic potential energy model for finite and extended
systems. In Proceedings of the 32nd International Conference on Neural Information Processing
Systems (NIPS'18). Curran Associates Inc., Red Hook, NY, USA, 4441-4451.
"""
def __init__(
self,
rcut: float,
rcut_smth: float,
sel: List[str],
neuron: List[int] = [24, 48, 96],
axis_neuron: int = 8,
resnet_dt: bool = False,
trainable: bool = True,
type_one_side: bool = True,
exclude_types: List[List[int]] = [],
set_davg_zero: bool = False,
activation_function: str = "tanh",
precision: str = DEFAULT_PRECISION,
spin: Optional[Any] = None,
) -> None:
## seed, uniform_seed, multi_task, not included.
if not type_one_side:
raise NotImplementedError("type_one_side == False not implemented")
if exclude_types != []:
raise NotImplementedError("exclude_types is not implemented")
if spin is not None:
raise NotImplementedError("spin is not implemented")
self.rcut = rcut
self.rcut_smth = rcut_smth
self.sel = sel
self.ntypes = len(self.sel)
self.neuron = neuron
self.axis_neuron = axis_neuron
self.resnet_dt = resnet_dt
self.trainable = trainable
self.type_one_side = type_one_side
self.exclude_types = exclude_types
self.set_davg_zero = set_davg_zero
self.activation_function = activation_function
self.precision = precision
self.spin = spin
in_dim = 1 # not considiering type embedding
self.embeddings = NetworkCollection(
ntypes=self.ntypes,
ndim=(1 if self.type_one_side else 2),
network_type="embedding_network",
)
for ii in range(self.ntypes):
self.embeddings[(ii,)] = EmbeddingNet(
in_dim,
self.neuron,
self.activation_function,
self.resnet_dt,
self.precision,
)
self.env_mat = EnvMat(self.rcut, self.rcut_smth)
self.nnei = np.sum(self.sel)
self.nneix4 = self.nnei * 4
self.davg = np.zeros([self.ntypes, self.nneix4])
self.dstd = np.ones([self.ntypes, self.nneix4])
self.orig_sel = self.sel
def __setitem__(self, key, value):
if key in ("avg", "data_avg", "davg"):
self.davg = value
elif key in ("std", "data_std", "dstd"):
self.dstd = value
else:
raise KeyError(key)
def __getitem__(self, key):
if key in ("avg", "data_avg", "davg"):
return self.davg
elif key in ("std", "data_std", "dstd"):
return self.dstd
else:
raise KeyError(key)
[docs] def cal_g(
self,
ss,
ll,
):
nf, nloc, nnei = ss.shape[0:3]
ss = ss.reshape(nf, nloc, nnei, 1)
# nf x nloc x nnei x ng
gg = self.embeddings[(ll,)].call(ss)
return gg
[docs] def call(
self,
coord_ext,
atype_ext,
nlist,
):
"""Compute the descriptor.
Parameters
----------
coord_ext
The extended coordinates of atoms. shape: nf x (nallx3)
atype_ext
The extended aotm types. shape: nf x nall
nlist
The neighbor list. shape: nf x nloc x nnei
Returns
-------
descriptor
The descriptor. shape: nf x nloc x ng x axis_neuron
"""
# nf x nloc x nnei x 4
rr, ww = self.env_mat.call(coord_ext, atype_ext, nlist, self.davg, self.dstd)
nf, nloc, nnei, _ = rr.shape
sec = np.append([0], np.cumsum(self.sel))
ng = self.neuron[-1]
gr = np.zeros([nf, nloc, ng, 4])
for tt in range(self.ntypes):
tr = rr[:, :, sec[tt] : sec[tt + 1], :]
ss = tr[..., 0:1]
gg = self.cal_g(ss, tt)
# nf x nloc x ng x 4
gr += np.einsum("flni,flnj->flij", gg, tr)
gr /= self.nnei
gr1 = gr[:, :, : self.axis_neuron, :]
# nf x nloc x ng x ng1
grrg = np.einsum("flid,fljd->flij", gr, gr1)
# nf x nloc x (ng x ng1)
grrg = grrg.reshape(nf, nloc, ng * self.axis_neuron)
return grrg
[docs] def serialize(self) -> dict:
return {
"rcut": self.rcut,
"rcut_smth": self.rcut_smth,
"sel": self.sel,
"neuron": self.neuron,
"axis_neuron": self.axis_neuron,
"resnet_dt": self.resnet_dt,
"trainable": self.trainable,
"type_one_side": self.type_one_side,
"exclude_types": self.exclude_types,
"set_davg_zero": self.set_davg_zero,
"activation_function": self.activation_function,
"precision": self.precision,
"spin": self.spin,
"env_mat": self.env_mat.serialize(),
"embeddings": self.embeddings.serialize(),
"@variables": {
"davg": self.davg,
"dstd": self.dstd,
},
}
[docs] @classmethod
def deserialize(cls, data: dict) -> "DescrptSeA":
data = copy.deepcopy(data)
variables = data.pop("@variables")
embeddings = data.pop("embeddings")
env_mat = data.pop("env_mat")
obj = cls(**data)
obj["davg"] = variables["davg"]
obj["dstd"] = variables["dstd"]
obj.embeddings = NetworkCollection.deserialize(embeddings)
obj.env_mat = EnvMat.deserialize(env_mat)
return obj