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sponge.core.WithForceCell

class sponge.core.WithForceCell(system: Molecule = None, force: ForceCell = None, neighbour_list: NeighbourList = None, modifier: ForceModifier = None)

Cell that wraps the simulation system with the atomic force function.

Parameters

• system (sponge.system.Molecule) – Simulation system.

• force (sponge.potential.ForceCell) – Atomic force calculation cell.

• neighbour_list (sponge.partition.NeighbourList, optional) – Neighbour list. Default: None.

• modifier (sponge.sampling.modifier.ForceModifier, optional) – Force modifier. Default: None.

Inputs:

• energy (Tensor) - Total potential energy of the simulation system. Tensor of shape $(B,1)$. Here B is batch size, i.e. the number of walkers in simulation. Data type is float.

• force (Tensor) - Data type is float.Force on each atoms of the simulation system. Tensor of shape $(B,A,D)$. Here $B$ is batch size, i.e. the number of walkers in simulation, A is the number of atoms, and $D$ is the spatial dimension of the simulation system, which is usually 3.

• virial (Tensor) - Virial tensor of the simulation system. Tensor of shape $(B,D)$. Data type is float.

Outputs:

• energy (Tensor) - with shape of $(B,1)$. Total potential energy of the simulation system. Data type is float.

• force (Tensor) - with shape of $(B,A,D)$. Force on each atoms of the simulation system. Data type is float.

• virial (Tensor) - with shape of $(B,D)$. Virial tensor of the simulation system. Data type is float.

Supported Platforms:

Ascend GPU

Examples

>>> # You can find case2.pdb file under MindSPONGE/tutorials/basic/case2.pdb
>>> from sponge import Protein
>>> from sponge.potential.forcefield import ForceField
>>> from sponge.partition import NeighbourList
>>> from sponge.core.simulation import WithEnergyCell, WithForceCell
>>> from sponge.sampling import MaskedDriven
>>> system = Protein(pdb='case2.pdb', rebuild_hydrogen=True)
>>> energy = ForceField(system, 'AMBER.FF99SB')
>>> neighbour_list = NeighbourList(system, cutoff=None, cast_fp16=True)
>>> with_energy = WithEnergyCell(system, energy, neighbour_list=neighbour_list)
>>> modifier = MaskedDriven(length_unit=with_energy.length_unit,
...                         energy_unit=with_energy.energy_unit,
...                         mask=system.heavy_atom_mask)
>>> with_force = WithForceCell(system, neighbour_list=neighbour_list, modifier=modifier)

property cutoff: mindspore.common.tensor.Tensor

Cutoff distance for neighbour list

Returns

Tensor, cutoff

property energy_unit: str

Energy unit

Returns

str, energy unit

get_neighbour_list()

Get neighbour list

Returns

• neigh_idx, Tensor of shape $(B,A,N)$. Index of neighbouring atoms of each atoms in system. Here $B$ is the number of walkers in simulation, A is the number of atoms, $N$ is the number of neighbouring atoms. Data type is int.

• neigh_mask, Tensor of shape $(B,A,N)$. Mask for neighbour list neigh_idx. Data type is bool.

property length_unit: str

Length unit

Returns

str, length unit

property neighbour_list_pace: int

Update step for neighbour list

Returns

int, step

set_pbc_grad(grad_box: bool)

Set whether to calculate the gradient of PBC box

Parameters

grad_box (bool) – Whether to calculate the gradient of PBC box.

update_modifier(step: int)

Update force modifier

Parameters

step (int) – Simulatio step.

update_neighbour_list()

Update neighbour list

Parameters

• coordinate (Tensor) – Position coordinate. Tensor of shape $(B,A,D)$. Here $B$ is the number of walkers in simulation, $A$ is the number of atoms, $D$ is the spatial dimension of the simulation system, which is usually 3. Data type is float.

• pbc_box (Tensor) – Size of PBC box. Tensor of shape $(B,D)$. Data type is float.

Returns

• neigh_idx, Tensor of shape $(B,A,N)$. Index of neighbouring atoms of each atoms in system. Here $N$ is the number of neighbouring atoms. Data type is int.

• neigh_mask, Tensor of shape $(B,A,N)$. Mask for neighbour list neigh_idx. Data type is bool.