deepmd.tf.nvnmd.entrypoints

deepmd.tf.nvnmd.entrypoints#

Submodules#

Classes#

`MapTable`	Generate the mapping table describing the relastionship of
`Wrap`	Generate the binary model file (model.pb).

Functions#

save_weight(→ None)

Save the dictionary of weight to a npy file.

Package Contents#

deepmd.tf.nvnmd.entrypoints.save_weight(sess: deepmd.tf.env.tf.Session, file_name: str = 'nvnmd/weight.npy') → None[source]#: Save the dictionary of weight to a npy file.

class deepmd.tf.nvnmd.entrypoints.MapTable(config_file: str, weight_file: str, map_file: str)[source]#

Generate the mapping table describing the relastionship of atomic distance, cutoff function, and embedding matrix.

three mapping table will be built:

\(r^2_{ji} \rightarrow s_{ji}\)
\(r^2_{ji} \rightarrow h_{ji}\)
\(r^2_{ji} \rightarrow \mathcal{G}_{ji}\)

where \(s_{ji}\) is cut-off function, \(h_{ji} = \frac{s(r_{ji})}{r_{ji}}\), and \(\mathcal{G}_{ji}\) is embedding matrix.

The mapping function can be define as:

\(y = f(x) = y_{k} + (x - x_{k}) * dy_{k}\)
\(y_{k} = f(x_{k})\)
\(dy_{k} = \frac{f(x_{k+1}) - f(x_{k})}{dx}\)
\(x_{k} \leq x < x_{k+1}\)
\(x_{k} = k * dx\)

where \(dx\) is interpolation interval.

Parameters:

config_file: input file name an .npy file containing the configuration information of NVNMD model
weight_file: input file name an .npy file containing the weights of NVNMD model
map_file: output file name an .npy file containing the mapping tables of NVNMD model

References

DOI: 10.1038/s41524-022-00773-z

config_file#

weight_file#

map_file#

Gs_Gt_mode = 1#

build_map() → dict[source]#

mapping(x: numpy.ndarray, dic_map: dict, cfgs: dict) → dict[source]#: Evaluate value by mapping table operation of tensorflow.

mapping2(x: numpy.ndarray, dic_map: dict, cfgs: dict) → dict[source]#: Evaluate value by mapping table of numpy.

plot_lines(x: numpy.ndarray, dic1: dict, dic2: dict | None = None) → None[source]#: Plot lines to see accuracy.

build_map_coef(cfgs: list, x: numpy.ndarray, ys: numpy.ndarray, grads: numpy.ndarray, grad_grads: numpy.ndarray, Nr: int, Nc: int) → tuple[numpy.ndarray, numpy.ndarray][source]#

Build mapping table coefficient cfgs: cfg list cfg = x0, x1, dx.

coef4: a x^3 + b x^2 + c x + d = y: / d = y0 | c = y0’ | b = (3 y1 - dx dy’ - 2dx y0’ - 3y0) / dx^2 a = (dx y1’ - 2 y1 + dx y0’ + 2 y0) / dx^3

cal_coef4(cfgs: list, x: numpy.ndarray, y: numpy.ndarray, dy: numpy.ndarray) → numpy.ndarray[source]#: Build mapping table coefficient for one line coef4: a x^3 + b x^2 + c x + d = y: / d = y0 | c = y0’ | b = (3 y1 - dx dy’ - 2dx y0’ - 3y0) / dx^2 a = (dx y1’ - 2 y1 + dx y0’ + 2 y0) / dx^3.

build_grad(x: Any, y: Any, Nr: int, Nc: int) → tuple[Any, Any][source]#: : Build gradient of tensor y of x.

build_u2s(r2: deepmd.tf.env.tf.Tensor) → deepmd.tf.env.tf.Tensor[source]#: Build tensor s, s=s(r2).

build_u2s_grad() → dict[source]#: Build gradient of s with respect to u (r^2).

run_u2s() → tuple[dict, dict][source]#: Build u->s graph and run it to get value of mapping table.

build_s2g(s: deepmd.tf.env.tf.Tensor) → deepmd.tf.env.tf.Tensor[source]#: Build s->G s is switch function G is embedding net output.

build_s2g_grad() → dict[source]#: Build gradient of G with respect to s.

run_s2g() → tuple[dict, dict][source]#: Build s-> graph and run it to get value of mapping table.

build_t2g() → dict[source]#: Build t->G t is chemical species of center atom and neighbor atom G is embedding net output of type.

run_t2g() → dict[source]#: Build t-> graph and run it to get value of mapping table.

build_embedding_net(xx: deepmd.tf.env.tf.Tensor, wbs: list, activation_fn: Callable | None = tf.tanh) → deepmd.tf.env.tf.Tensor[source]#

build_davg_dstd() → dict[source]#

class deepmd.tf.nvnmd.entrypoints.Wrap(config_file: str, weight_file: str, map_file: str, model_file: str)[source]#

Generate the binary model file (model.pb).

the model file can be use to run the NVNMD with lammps the pair style need set as:

pair_style nvnmd model.pb
pair_coeff * *

Parameters:

config_file: input file name an .npy file containing the configuration information of NVNMD model
weight_file: input file name an .npy file containing the weights of NVNMD model
map_file: input file name an .npy file containing the mapping tables of NVNMD model
model_file: output file name an .pb file containing the model using in the NVNMD

References

DOI: 10.1038/s41524-022-00773-z

config_file#

weight_file#

map_file#

model_file#

wrap() → None[source]#

wrap_head(nhs: Any, nws: Any) → Any[source]#

Wrap the head information.

version nhead nheight nwidth rcut cut-off radius ntype number of atomic species nnei number of neighbors atom_ener atom bias energy ener_fact factor for atom_ener

wrap_dscp() → str[source]#

Wrap the configuration of descriptor.

version 0: [NBIT_IDX_S2G-1:0] SHIFT_IDX_S2G

[NBIT_NEIB*NTYPE-1:0] SELs [NBIT_FIXD*M1*NTYPE*NTYPE-1:0] GSs [NBIT_FLTE-1:0] NEXPO_DIV_NI

version 1:

[NBIT_FLTE-1:0] NEXPO_DIV_NI

wrap_fitn() → tuple[list[str], list[str]][source]#

Wrap the weights of fitting net.

w weight b bias

wrap_bias(bias: numpy.ndarray, NBIT_DATA: int, NBIT_DATA_FL: int) → list[str][source]#

wrap_weight(weight: numpy.ndarray, NBIT_DISP: int, NBIT_WEIGHT: int) → tuple[list[str], list[str], list[list[str]], list[list[str]]][source]#: weight: weights of fittingNet NBIT_DISP: nbits of exponent of weight max value NBIT_WEIGHT: nbits of mantissa of weights.

wrap_map() → tuple[list[str], list[str], list[str], list[str]][source]#: Wrap the mapping table of embedding network.

wrap_lut() → tuple[list[str], list[str], list[str]][source]#: Wrap the LUT.