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Adjust the output of MD simulation

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pull/11666/head
zhangxinfeng3 4 years ago
parent 74e4cc1876
commit 0758a95d4e
4 changed files with 4 additions and 86 deletions
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5
model_zoo/research/hpc/molecular_dynamics/README.md
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@ -13,7 +13,7 @@
## Description
Molecular Dynamics (MD) is playing an increasingly important role in the research of biology, pharmacy, chemistry, and materials science. The architecture is based on DeePMD, which using an NN scheme for MD simulations, which overcomes the limitations associated to auxiliary quantities like the symmetry functions or the Coulomb matrix. Each environment contains a number of atoms, whose local coordinates are arranged in a symmetry preserving way following the prescription of the Deep Potential method. According to the atomic position, atomic types and box tensor to construct energy, force and virial.
Molecular Dynamics (MD) is playing an increasingly important role in the research of biology, pharmacy, chemistry, and materials science. The architecture is based on DeePMD, which using an NN scheme for MD simulations, which overcomes the limitations associated to auxiliary quantities like the symmetry functions or the Coulomb matrix. Each environment contains a number of atoms, whose local coordinates are arranged in a symmetry preserving way following the prescription of the Deep Potential method. According to the atomic position, atomic types and box tensor to construct energy.
Thanks a lot for DeePMD team's help.
@ -75,7 +75,6 @@ In `deepmodeling/deepmd-kit/source`:
│ ├── eval.sh # evaluation script
├── src
│ ├── descriptor.py # descriptor function
│ ├── virial.py # calculating virial function
│ └── network.py # MD simulation architecture
└── eval.py # evaluation interface
```
@ -101,8 +100,6 @@ The infer result：
```text
energy: -29944.03
atom_energy: -94.38766 -94.294426 -94.39194 -94.70758 -94.51311 -94.457954 ...
force: 1.649112 -1.0982257 0.46055675 -1.3491507 -0.3382736 -0.97184074 ...
virial: -11.736662 -4.2862144 2.8852935 -4.286209 -10.408775 -5.6738224 ...
```
## ModelZoo Homepage

4
model_zoo/research/hpc/molecular_dynamics/eval.py
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@ -49,9 +49,7 @@ if __name__ == '__main__':
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.to_float(mstype.float32)
energy, atom_ener, force, virial = \
energy, atom_ener, _ = \
net(d_coord_tensor, d_nlist_tensor, frames, avg_tensor, std_tensor, atype_tensor, nlist_tensor)
print('energy:', energy)
print('atom_energy:', atom_ener)
print('force:', force)
print('virial:', virial)

13
model_zoo/research/hpc/molecular_dynamics/src/network.py
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@ -22,7 +22,6 @@ from mindspore import Tensor
from mindspore import nn
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from .virial import ProdVirialSeA
from .descriptor import DescriptorSeA
natoms = [192, 192, 64, 128]
@ -230,12 +229,10 @@ class Network(nn.Cell):
self.grad = Grad(self.mdnet)
self.descrpt_se_a = DescriptorSeA()
self.process = Processing()
self.prod_virial_se_a = ProdVirialSeA()
self.prod_force_se_a = P.ProdForceSeA()
def construct(self, d_coord, d_nlist, frames, avg, std, atype, nlist):
"""construct function."""
rij, descrpt, descrpt_deriv = \
_, descrpt, _ = \
self.descrpt_se_a(d_coord, d_nlist, frames, avg, std, atype)
# calculate energy and atom_ener
atom_ener = self.mdnet(descrpt)
@ -244,10 +241,4 @@ class Network(nn.Cell):
energy = self.sum(energy_raw, 1)
# grad of atom_ener
net_deriv = self.grad(descrpt)
net_deriv_reshape = self.reshape(net_deriv[0], (-1, natoms[0], ndescrpt))
descrpt_deriv_reshape = self.reshape(descrpt_deriv, (-1, natoms[0], ndescrpt, 3))
# calculate virial
virial = self.prod_virial_se_a(net_deriv_reshape, descrpt_deriv_reshape, rij, nlist)
# calculate force
force = self.prod_force_se_a(net_deriv_reshape, descrpt_deriv_reshape, nlist)
return energy, atom_ener, force, virial
return energy, atom_ener, net_deriv

68
model_zoo/research/hpc/molecular_dynamics/src/virial.py
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@ -1,68 +0,0 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Calculate virial of atoms."""
import mindspore.common.dtype as mstype
from mindspore import nn
from mindspore.ops import operations as P
class ProdVirialSeA(nn.Cell):
"""calculate virial."""
def __init__(self):
super(ProdVirialSeA, self).__init__()
self.reshape = P.Reshape()
self.transpose = P.Transpose()
self.cast = P.Cast()
self.rsum = P.ReduceSum()
self.rksum = P.ReduceSum(keep_dims=True)
self.broadcastto1 = P.BroadcastTo((1, 192, 138, 4, 3, 3))
self.broadcastto2 = P.BroadcastTo((1, 192, 138, 4, 3))
self.broadcastto3 = P.BroadcastTo((1, 192, 138, 3))
self.expdims = P.ExpandDims()
def construct(self, net_deriv_reshape, descrpt_deriv, rij, nlist):
"""construct function."""
descrpt_deriv = self.cast(descrpt_deriv, mstype.float32)
descrpt_deriv = self.reshape(descrpt_deriv, (1, 192, 138, 4, 3))
net_deriv_reshape = self.cast(net_deriv_reshape, mstype.float32)
net_deriv_reshape = self.reshape(net_deriv_reshape, (1, 192, 138, 4))
net_deriv_reshape = self.expdims(net_deriv_reshape, 4)
net_deriv_reshape = self.broadcastto2(net_deriv_reshape)
rij = self.cast(rij, mstype.float32)
rij = self.reshape(rij, (1, 192, 138, 3))
rij = self.expdims(rij, 3)
rij = self.expdims(rij, 4)
rij = self.broadcastto1(rij)
nlist = self.cast(nlist, mstype.int32)
nlist = self.reshape(nlist, (1, 192, 138))
nlist = self.expdims(nlist, 3)
nlist = self.broadcastto3(nlist)
tmp = descrpt_deriv * net_deriv_reshape
b_blist = self.cast(nlist > -1, mstype.int32)
b_blist = self.expdims(b_blist, 3)
b_blist = self.broadcastto2(b_blist)
tmp_1 = tmp * b_blist
tmp_1 = self.expdims(tmp_1, 5)
tmp_1 = self.broadcastto1(tmp_1)
out = tmp_1 * rij
out = self.rsum(out, (1, 2, 3))
return out
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