python.md

Python interface

One may use the python interface of DeePMD-kit for model inference, an example is given as follows

from deepmd.infer import DeepPot
import numpy as np

dp = DeepPot("graph.pb")
coord = np.array([[1, 0, 0], [0, 0, 1.5], [1, 0, 3]]).reshape([1, -1])
cell = np.diag(10 * np.ones(3)).reshape([1, -1])
atype = [1, 0, 1]
e, f, v = dp.eval(coord, cell, atype)

where e, f and v are predicted energy, force and virial of the system, respectively.

Furthermore, one can use the python interface to calculate model deviation.

from deepmd.infer import calc_model_devi
from deepmd.infer import DeepPot as DP
import numpy as np

coord = np.array([[1, 0, 0], [0, 0, 1.5], [1, 0, 3]]).reshape([1, -1])
cell = np.diag(10 * np.ones(3)).reshape([1, -1])
atype = [1, 0, 1]
graphs = [DP("graph.000.pb"), DP("graph.001.pb")]
model_devi = calc_model_devi(coord, cell, atype, graphs)

Note that if the model inference or model deviation is performed cyclically, one should avoid calling the same model multiple times. Otherwise, tensorFlow will never release the memory and this may lead to an out-of-memory (OOM) error.

External neighbor list algorithm

The native neighbor list algorithm of the DeePMD-kit is in $O(N^2)$ complexity ($N$ is the number of atoms). While this is not a problem for small systems that quantum methods can afford, the large systems for molecular dynamics have slow performance. In this case, one may pass an external neighbor list that has lower complexity to {class}DeepPot <deepmd.infer.DeepPot>, once it is compatible with {class}ase.neighborlist.NewPrimitiveNeighborList.

import ase.neighborlist

neighbor_list = ase.neighborlist.NewPrimitiveNeighborList(
    cutoffs=6, bothways=True, self_interaction=False
)
dp = DeepPot("graph.pb", neighbor_list=neighbor_list)

The update and build methods will be called by {class}DeepPot <deepmd.infer.DeepPot>, and first_neigh, pair_second, and offset_vec properties will be used.