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Unverified Commit 279e53ad authored by Christian Devereux's avatar Christian Devereux Committed by GitHub
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Ani 2x (#480) 



* Adding ANI-2x parameters

* Moving 2x to seperate branch

* Adding ANI-2x to new branch

* Adding 2x network

* Update models.py

* Update api.rst

* Update __init__.py
Co-authored-by: default avatarChristian Devereux <cdever01@ufl.edu>
Co-authored-by: default avatarFarhad Ramezanghorbani <farhadrgh@users.noreply.github.com>
parent 13cacd04
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docs/api.rst
View file @ 279e53ad
...@@ -20,6 +20,8 @@ Model Zoo ...@@ -20,6 +20,8 @@ Model Zoo
:members: :members:
.. autoclass:: torchani.models.ANI1ccx .. autoclass:: torchani.models.ANI1ccx
:members: :members:
.. autoclass:: torchani.models.ANI2x
:members:
Datasets Datasets
......
torchani/__init__.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
"""TorchANI is a PyTorch implementation of `ANI`_, created and maintained by """`TorchANI`_ is a PyTorch implementation of `ANI`_, created and maintained by
the `Roitberg group`_. TorchANI contains classes like the `Roitberg group`_. TorchANI contains classes like
:class:`AEVComputer`, :class:`ANIModel`, and :class:`EnergyShifter` that can :class:`AEVComputer`, :class:`ANIModel`, and :class:`EnergyShifter` that can
be pipelined to compute molecular energies from the 3D coordinates of be pipelined to compute molecular energies from the 3D coordinates of
molecules. It also include tools to: deal with ANI datasets(e.g. `ANI-1`_, molecules. It also include tools to: deal with ANI datasets(e.g. `ANI-1`_,
`ANI-1x`_, `ANI-1ccx`_, etc.) at :attr:`torchani.data`, import various file `ANI-1x`_, `ANI-1ccx`_, `ANI-2x`_) at :attr:`torchani.data`, import various file
formats of NeuroChem at :attr:`torchani.neurochem`, and more at :attr:`torchani.utils`. formats of NeuroChem at :attr:`torchani.neurochem`, and more at :attr:`torchani.utils`.
.. _TorchANI:
https://doi.org/10.26434/chemrxiv.12218294.v1
.. _ANI: .. _ANI:
http://pubs.rsc.org/en/Content/ArticleLanding/2017/SC/C6SC05720A#!divAbstract http://pubs.rsc.org/en/Content/ArticleLanding/2017/SC/C6SC05720A#!divAbstract
...@@ -21,6 +24,9 @@ formats of NeuroChem at :attr:`torchani.neurochem`, and more at :attr:`torchani. ...@@ -21,6 +24,9 @@ formats of NeuroChem at :attr:`torchani.neurochem`, and more at :attr:`torchani.
.. _ANI-1ccx: .. _ANI-1ccx:
https://doi.org/10.26434/chemrxiv.6744440.v1 https://doi.org/10.26434/chemrxiv.6744440.v1
.. _ANI-2x:
https://doi.org/10.26434/chemrxiv.11819268.v1
""" """
from .utils import EnergyShifter from .utils import EnergyShifter
......
torchani/models.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
"""The ANI model zoo that stores public ANI models. """The ANI model zoo that stores public ANI models.
Currently the model zoo has two models: ANI-1x and ANI-1ccx. The classes Currently the model zoo has three models: ANI-1x, ANI-1ccx, and ANI-2x.
of these two models are :class:`ANI1x` and :class:`ANI1ccx`, The classes of these models are :class:`ANI1x`, :class:`ANI1ccx`, and
these are subclasses of :class:`torch.nn.Module`. :class:`ANI2x` these are subclasses of :class:`torch.nn.Module`.
To use the models just instantiate them and either To use the models just instantiate them and either
directly calculate energies or get an ASE calculator. For example: directly calculate energies or get an ASE calculator. For example:
...@@ -282,3 +282,23 @@ def ANI1ccx(periodic_table_index=False, model_index=None): ...@@ -282,3 +282,23 @@ def ANI1ccx(periodic_table_index=False, model_index=None):
if model_index is None: if model_index is None:
return BuiltinEnsemble._from_neurochem_resources(info_file, periodic_table_index) return BuiltinEnsemble._from_neurochem_resources(info_file, periodic_table_index)
return BuiltinModel._from_neurochem_resources(info_file, periodic_table_index, model_index) return BuiltinModel._from_neurochem_resources(info_file, periodic_table_index, model_index)
def ANI2x(periodic_table_index=False, model_index=None):
"""The ANI-2x model as in `ANI2x Paper`_ and `ANI2x Results on GitHub`_.
The ANI-2x model is an ensemble of 8 networks that was trained on the
ANI-2x dataset. The target level of theory is wB97X/6-31G(d). It predicts
energies on HCNOFSCl elements exclusively it shouldn't be used with other
atom types.
.. _ANI2x Results on GitHub:
https://github.com/cdever01/ani-2x_results
.. _ANI2x Paper:
https://doi.org/10.26434/chemrxiv.11819268.v1
"""
info_file = 'ani-2x_8x.info'
if model_index is None:
return BuiltinEnsemble._from_neurochem_resources(info_file, periodic_table_index)
return BuiltinModel._from_neurochem_resources(info_file, periodic_table_index, model_index)
torchani/resources/ani-2x_8x.info 0 → 100644
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ani-2x_8x/rHCNOSFCl-5.1R_16-3.5A_a8-4.params
ani-2x_8x/sae_linfit.dat
ani-2x_8x/train
8
torchani/resources/ani-2x_8x/inputtrain.ipt 0 → 100644
View file @ 279e53ad
sflparamsfile=rHCNOSFCl-5.1R_16-3.5A_a8-4.params
ntwkStoreDir=networks/
atomEnergyFile=sae_linfit.dat
nmax=0
tolr=40
emult=5.0000000e-01
eta=0.001
tcrit=1.0000000e-05
tmax=0
tbtchsz=2048
vbtchsz=2048
gpuid=0
ntwshr=0
nkde=0
energy=1
force=1
dipole=0
charge=0
acachg=0
fmult=0.001
pbc=0
cmult=1.0000000e+00
runtype=ANNP_CREATE_HDNN_AND_TRAIN
adptlrn=OFF
decrate=9.0000000e-01
moment=ADAM
mu=9.9000000e-01
cdweight=0.1
repuls=0
seed=1065533848
network_setup {
inputsize=1008;
atom_net H $
layer [
nodes=256;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=192;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=160;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net C $
layer [
nodes=224;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=192;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=160;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net N $
layer [
nodes=192;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=160;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net O $
layer [
nodes=192;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=160;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net S $
layer [
nodes=160;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=96;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net F $
layer [
nodes=160;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=96;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net Cl $
layer [
nodes=160;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=96;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
}
torchani/resources/ani-2x_8x/rHCNOSFCl-5.1R_16-3.5A_a8-4.params 0 → 100644
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DESC = ANI_NORMAL
TM = 1
CG = 0
RP = 0
AC = 0
Rcr = 5.1000e+00
Rca = 3.5000e+00
EtaR = [1.9700000e+01]
ShfR = [8.0000000e-01,1.0687500e+00,1.3375000e+00,1.6062500e+00,1.8750000e+00,2.1437500e+00,2.4125000e+00,2.6812500e+00,2.9500000e+00,3.2187500e+00,3.4875000e+00,3.7562500e+00,4.0250000e+00,4.2937500e+00,4.5625000e+00,4.8312500e+00]
Zeta = [1.4100000e+01]
ShfZ = [3.9269908e-01,1.1780972e+00,1.9634954e+00,2.7488936e+00]
EtaA = [1.2500000e+01]
ShfA = [8.0000000e-01,1.1375000e+00,1.4750000e+00,1.8125000e+00,2.1500000e+00,2.4875000e+00,2.8250000e+00,3.1625000e+00]
Atyp = [H,C,N,O,S,F,Cl]
torchani/resources/ani-2x_8x/sae_linfit.dat 0 → 100644
View file @ 279e53ad
H,0=-0.5978583943827134
C,1=-38.08933878049795
N,2=-54.711968298621066
O,3=-75.19106774742086
S,4=-398.1577125334925
F,5=-99.80348506781634
Cl,6=-460.1681939421027
torchani/resources/ani-2x_8x/train0/cost.dat 0 → 100644
View file @ 279e53ad
1 0.00522834 0.000167837 1.79769e+308 1.79769e+308 inf inf
2 0.00018561 9.09588e-05 1.79769e+308 1.79769e+308 inf inf
3 0.0157516 0.00024698 9.09588e-05 0.000326497 5.9847 11.3386
4 0.000417343 0.000183088 9.09588e-05 0.000326497 5.9847 11.3386
5 0.00420965 0.000355009 9.09588e-05 0.000326497 5.9847 11.3386
6 0.000255287 0.00011002 9.09588e-05 0.000326497 5.9847 11.3386
7 0.000212257 0.000215678 9.09588e-05 0.000326497 5.9847 11.3386
8 0.000140857 5.5339e-05 9.09588e-05 0.000326497 5.9847 11.3386
9 0.000139732 4.97517e-05 5.5339e-05 0.000221038 4.66805 9.32939
10 9.15171e-05 7.01375e-05 4.97517e-05 0.000191665 4.42613 8.68744
11 0.000114673 3.55637e-05 4.97517e-05 0.000191665 4.42613 8.68744
12 9.34657e-05 5.48231e-05 3.55637e-05 0.000141896 3.74217 7.4749
13 7.45478e-05 6.83138e-05 3.55637e-05 0.000141896 3.74217 7.4749
14 0.000113433 4.4885e-05 3.55637e-05 0.000141896 3.74217 7.4749
15 6.74438e-05 3.09375e-05 3.55637e-05 0.000141896 3.74217 7.4749
16 7.4312e-05 2.52397e-05 3.09375e-05 0.000121972 3.4903 6.93027
17 5.8468e-05 2.41203e-05 2.52397e-05 0.000110796 3.15255 6.60514
18 8.55237e-05 3.55376e-05 2.41203e-05 0.000101281 3.08185 6.31517
19 5.22953e-05 2.90651e-05 2.41203e-05 0.000101281 3.08185 6.31517
20 5.331e-05 3.40845e-05 2.41203e-05 0.000101281 3.08185 6.31517
21 4.86309e-05 2.21835e-05 2.41203e-05 0.000101281 3.08185 6.31517
22 6.92644e-05 2.07832e-05 2.21835e-05 8.70455e-05 2.95553 5.85455
23 4.5967e-05 2.74133e-05 2.07832e-05 8.5811e-05 2.86073 5.81289
24 4.96718e-05 2.38755e-05 2.07832e-05 8.5811e-05 2.86073 5.81289
25 5.04561e-05 0.000159157 2.07832e-05 8.5811e-05 2.86073 5.81289
26 8.69752e-05 3.00027e-05 2.07832e-05 8.5811e-05 2.86073 5.81289
27 3.68898e-05 4.50093e-05 2.07832e-05 8.5811e-05 2.86073 5.81289
28 6.1702e-05 2.08568e-05 2.07832e-05 8.5811e-05 2.86073 5.81289
29 3.44444e-05 1.96136e-05 2.07832e-05 8.5811e-05 2.86073 5.81289
30 7.02587e-05 2.18674e-05 1.96136e-05 7.60913e-05 2.77907 5.47378
31 3.84023e-05 2.14172e-05 1.96136e-05 7.60913e-05 2.77907 5.47378
32 0.000133743 2.65993e-05 1.96136e-05 7.60913e-05 2.77907 5.47378
33 4.0286e-05 2.7442e-05 1.96136e-05 7.60913e-05 2.77907 5.47378
34 3.93895e-05 2.06079e-05 1.96136e-05 7.60913e-05 2.77907 5.47378
35 3.93903e-05 1.69464e-05 2.06079e-05 7.13337e-05 2.84864 5.2999
36 6.74307e-05 5.03395e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
37 3.77803e-05 3.49628e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
38 7.84914e-05 2.15433e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
39 3.75518e-05 1.6651e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
40 4.07534e-05 1.82939e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
41 5.93064e-05 2.11695e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
42 3.52145e-05 3.06768e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
43 4.41246e-05 2.47765e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
44 4.65676e-05 1.8866e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
45 2.81244e-05 6.58357e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
46 6.66264e-05 2.10879e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
47 3.4647e-05 1.61905e-05 1.69464e-05 6.92777e-05 2.58321 5.22296
48 4.45064e-05 2.439e-05 1.61905e-05 6.72035e-05 2.52493 5.14418
49 3.17649e-05 4.02911e-05 1.61905e-05 6.72035e-05 2.52493 5.14418
50 3.31493e-05 2.55781e-05 1.61905e-05 6.72035e-05 2.52493 5.14418
51 3.76803e-05 2.27961e-05 1.61905e-05 6.72035e-05 2.52493 5.14418
52 4.76549e-05 1.60595e-05 1.61905e-05 6.72035e-05 2.52493 5.14418
53 3.11306e-05 3.58126e-05 1.60595e-05 6.45027e-05 2.5147 5.03975
54 7.76917e-05 1.85432e-05 1.60595e-05 6.45027e-05 2.5147 5.03975
55 2.30439e-05 4.7815e-05 1.60595e-05 6.45027e-05 2.5147 5.03975
56 5.7714e-05 3.13456e-05 1.60595e-05 6.45027e-05 2.5147 5.03975
57 2.3099e-05 1.48942e-05 1.60595e-05 6.45027e-05 2.5147 5.03975
58 3.04507e-05 2.20097e-05 1.48942e-05 6.01877e-05 2.42175 4.86826
59 4.45744e-05 1.72313e-05 1.48942e-05 6.01877e-05 2.42175 4.86826
60 4.47889e-05 1.33176e-05 1.48942e-05 6.01877e-05 2.42175 4.86826
61 2.67795e-05 6.22197e-05 1.33176e-05 5.96679e-05 2.28999 4.8472
62 3.50194e-05 1.53326e-05 1.33176e-05 5.96679e-05 2.28999 4.8472
63 5.60725e-05 1.72109e-05 1.33176e-05 5.96679e-05 2.28999 4.8472
64 2.71451e-05 1.50748e-05 1.33176e-05 5.96679e-05 2.28999 4.8472
65 3.24089e-05 1.33705e-05 1.33176e-05 5.96679e-05 2.28999 4.8472
66 7.08357e-05 1.77732e-05 1.33705e-05 5.72512e-05 2.29454 4.74802
67 2.61845e-05 4.19822e-05 1.33705e-05 5.72512e-05 2.29454 4.74802
68 2.95769e-05 1.70666e-05 1.33705e-05 5.72512e-05 2.29454 4.74802
69 5.18921e-05 2.73192e-05 1.33705e-05 5.72512e-05 2.29454 4.74802
70 2.99332e-05 1.32315e-05 1.33705e-05 5.72512e-05 2.29454 4.74802
71 2.8407e-05 2.35611e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
72 6.39116e-05 2.94289e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
73 3.00162e-05 8.43576e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
74 4.72109e-05 7.24236e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
75 2.82963e-05 4.21113e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
76 5.40558e-05 1.30462e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
77 2.42173e-05 1.5226e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
78 4.66508e-05 1.38197e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
79 2.46658e-05 1.71172e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
80 9.30285e-05 2.10412e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
81 2.75778e-05 4.51493e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
82 9.94219e-05 3.83532e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
83 2.5723e-05 2.37299e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
84 2.88261e-05 1.6076e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
85 5.38535e-05 4.38605e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
86 2.80417e-05 1.28448e-05 1.32315e-05 5.72876e-05 2.28257 4.74953
87 3.9443e-05 1.35339e-05 1.28448e-05 5.49222e-05 2.24897 4.65044
88 2.74372e-05 1.26759e-05 1.28448e-05 5.49222e-05 2.24897 4.65044
89 5.10627e-05 2.17381e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
90 2.3448e-05 1.59327e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
91 7.30201e-05 2.21799e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
92 2.79777e-05 1.71197e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
93 3.91585e-05 1.39661e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
94 2.32562e-05 3.0815e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
95 5.71986e-05 1.52757e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
96 2.62213e-05 1.66184e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
97 0.000105582 1.46888e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
98 2.67775e-05 1.6886e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
99 5.36697e-05 1.64703e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
100 2.0844e-05 0.000243936 1.26759e-05 5.49712e-05 2.23413 4.65251
101 3.49718e-05 3.32526e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
102 2.60302e-05 3.9492e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
103 0.000105877 1.78015e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
104 2.69549e-05 1.9971e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
105 3.91588e-05 0.000106947 1.26759e-05 5.49712e-05 2.23413 4.65251
106 3.84542e-05 1.30484e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
107 2.18453e-05 1.48045e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
108 2.66548e-05 6.85998e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
109 3.14206e-05 1.22298e-05 1.26759e-05 5.49712e-05 2.23413 4.65251
110 3.99922e-05 1.59012e-05 1.22298e-05 5.24328e-05 2.19448 4.54383
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927 6.67245e-06 8.28355e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
928 6.67345e-06 8.24064e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
929 6.68149e-06 8.2397e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
930 6.68852e-06 8.2283e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
931 6.68854e-06 8.1965e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
932 6.69111e-06 8.54438e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
933 6.70796e-06 8.23962e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
934 6.7093e-06 8.39612e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
935 6.7105e-06 8.23523e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
936 6.7146e-06 8.46037e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
937 6.72235e-06 8.28278e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
938 6.72915e-06 8.38084e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
939 6.73162e-06 8.63315e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
940 6.73674e-06 8.26959e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
941 6.74264e-06 9.03426e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
942 6.7533e-06 8.23997e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
943 6.75615e-06 8.27731e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
944 6.75823e-06 8.41296e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
945 6.76128e-06 8.45516e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
946 6.76486e-06 8.27558e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
947 6.7729e-06 8.27079e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
948 6.77601e-06 8.3155e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
949 6.78462e-06 8.3026e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
950 6.79001e-06 8.2593e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
951 6.78862e-06 8.32786e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
952 6.80263e-06 8.38665e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
953 6.80338e-06 8.2311e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
954 6.81299e-06 8.31178e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
955 6.82085e-06 8.47957e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
956 6.80657e-06 8.4108e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
957 6.82615e-06 8.66736e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
958 6.8291e-06 8.26506e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
959 6.83458e-06 8.28807e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
960 6.83675e-06 8.3356e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
961 6.84026e-06 8.37372e-06 8.15181e-06 3.46791e-05 1.79163 3.69534
torchani/resources/ani-2x_8x/train0/inputtrain.ipt 0 → 100644
View file @ 279e53ad
sflparamsfile=rHCNOSFCl-5.1R_16-3.5A_a8-4.params
ntwkStoreDir=networks/
atomEnergyFile=sae_linfit.dat
nmax=0
tolr=40
emult=5.0000000e-01
eta=0.001
tcrit=1.0000000e-05
tmax=0
tbtchsz=2048
vbtchsz=2048
gpuid=0
ntwshr=0
nkde=0
energy=1
force=1
dipole=0
charge=0
acachg=0
fmult=0.001
pbc=0
cmult=1.0000000e+00
runtype=ANNP_CREATE_HDNN_AND_TRAIN
adptlrn=OFF
decrate=9.0000000e-01
moment=ADAM
mu=9.9000000e-01
cdweight=0.1
repuls=0
seed=1065533848
network_setup {
inputsize=1008;
atom_net H $
layer [
nodes=256;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=192;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=160;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net C $
layer [
nodes=224;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=192;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=160;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net N $
layer [
nodes=192;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=160;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net O $
layer [
nodes=192;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=160;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net S $
layer [
nodes=160;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=96;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net F $
layer [
nodes=160;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=96;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
atom_net Cl $
layer [
nodes=160;
activation=9;
type=0;
l2norm=1;
l2valu=1.0000000e-06;
]
layer [
nodes=128;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=96;
activation=9;
type=0;
l2norm=0;
l2valu=1.0000000e-06;
]
layer [
nodes=1;
activation=6;
type=0;
]
$
}
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