Closed wanfuse123 closed 7 months ago
Hi, does the link in the README (https://github.com/learningmatter-mit/NeuralForceField) work for you?
Exception reporting mode: Verbose ['AttentionPool', 'BatchedPreActivation', 'BondPrior', 'ChemPropConv', 'ChemPropInit', 'ChemPropMsgToNode', 'ConfAttention', 'CosineEnvelope', 'CpSchNetConv', 'DEFAULT_BONDPRIOR_PARAM', 'DEFAULT_DROPOUT_RATE', 'DataLoader', 'Dense', 'DiabaticReadout', 'Diagonalize', 'DimeNet', 'DimeNetDelta', 'DimeNetDiabat', 'DimeNetDiabatDelta', 'DimeNetRadialBasis', 'DimeNetSphericalBasis', 'DirectedMessage', 'DirectedMessagePP', 'EPS', 'EPSILON', 'EdgeEmbedding', 'EdgeUpdateModule', 'EmbeddingBlock', 'Envelope', 'ExpNormalBasis', 'F', 'Gaussian', 'GaussianSmearing', 'GraphAttention', 'InteractionBlock', 'LeakyReLU', 'Linear', 'LinearConfAttention', 'MeanPool', 'MessagePassingModule', 'MixedSchNetConv', 'ModuleDict', 'MolFpPool', 'NodeEmbedding', 'NodeMultiTaskReadOut', 'OutputBlock', 'PainnRadialBasis', 'PreActivation', 'RadialBasis', 'ReLU', 'ResidualBlock', 'ScaleShift', 'SchNet', 'SchNetConv', 'SchNetDiabat', 'SchNetEdgeFilter', 'SchNetEdgeUpdate', 'Sequential', 'Softmax', 'SphericalBasis', 'StochasticIncrease', 'SumPool', 'TestModules', 'builtins', 'cached', 'doc', 'file', 'loader', 'name', 'package', 'path', 'spec', 'activations', 'add_stress', 'adj_nbrs_and_z', 'att_readout_probs', 'batch_and_sum', 'bessel_basis', 'chemprop_msg_to_node', 'chemprop_msg_update', 'clean_matrix', 'collections', 'compute_angle', 'compute_grad', 'compute_hess', 'computejacobian', 'constant', 'construct_module_dict', 'construct_sequential', 'copy', 'diabat', 'dimenet', 'gaussian_smearing', 'general_batched_hessian', 'get_act', 'get_default_readout', 'get_dense', 'get_offsets', 'get_painn_hessians', 'get_rij', 'get_schnet_hessians', 'grad', 'graphconv', 'graphop', 'hess_from_atoms', 'hess_from_pad', 'hess_from_results', 'inspect', 'lattice_points_in_supercell', 'layer_types', 'layers', 'models', 'modules', 'nn', 'np', 'pad', 'partial', 'real_sph_harm', 'remove_bias', 'results_from_stack', 'scatter_add', 'schnet', 'schnet_batched_hessians', 'shifted_softplus', 'single_spec_nbrs', 'softmax', 'sum_and_grad', 'sym', 'tensorgrad', 'to_module', 'torch', 'torch_nbr_list', 'unittest', 'utils', 'xavieruniform', 'zeros_initializer'] 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0]
CondaError: Run 'conda init' before 'conda activate'
no change /home/top/miniconda3/condabin/conda no change /home/top/miniconda3/bin/conda no change /home/top/miniconda3/bin/conda-env no change /home/top/miniconda3/bin/activate no change /home/top/miniconda3/bin/deactivate no change /home/top/miniconda3/etc/profile.d/conda.sh no change /home/top/miniconda3/etc/fish/conf.d/conda.fish no change /home/top/miniconda3/shell/condabin/Conda.psm1 no change /home/top/miniconda3/shell/condabin/conda-hook.ps1 no change /home/top/miniconda3/lib/python3.11/site-packages/xontrib/conda.xsh no change /home/top/miniconda3/etc/profile.d/conda.csh no change /home/top/.bashrc No action taken.
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pydash 7.0.6 pyh1a96a4e_0 conda-forge
pyflakes 3.1.0 pyhd8ed1ab_0 conda-forge
pygments 2.17.2 pyhd8ed1ab_0 conda-forge
pylint 3.0.3 pyhd8ed1ab_0 conda-forge
pymatgen 2023.5.10 py38h905acbe_0 conda-forge
pymc 5.6.1 hd8ed1ab_0 conda-forge
pymc-base 5.6.1 pyhd8ed1ab_0 conda-forge
pymongo 4.6.1 py38h17151c0_0 conda-forge
pynacl 1.5.0 py38h01eb140_3 conda-forge
pynng 0.7.2 py38h01eb140_1 conda-forge
pyopenssl 23.3.0 pyhd8ed1ab_0 conda-forge
pyparsing 3.1.1 pyhd8ed1ab_0 conda-forge
pyqt 5.15.9 py38hffdaa6c_5 conda-forge
pyqt5-sip 12.12.2 py38h17151c0_5 conda-forge
pysocks 1.7.1 pyha2e5f31_6 conda-forge
pytensor 2.13.1 py38h1128e8f_0
pytensor-base 2.12.3 py38h01efb38_3 conda-forge
python 3.8.18 hd12c33a_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-dotenv 1.0.0 pyhd8ed1ab_1 conda-forge
python-fastjsonschema 2.19.0 pyhd8ed1ab_0 conda-forge
python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge
python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge
python_abi 3.8 4_cp38 conda-forge
pytorch 2.0.0 cuda118py38hde7dd16_304 conda-forge
pytorch-cuda 11.7 h778d358_5 pytorch
pytz 2023.3.post1 pyhd8ed1ab_0 conda-forge
pyyaml 6.0.1 py38h01eb140_1 conda-forge
pyzmq 25.1.2 py38h34c975a_0 conda-forge
qt-main 5.15.8 h82b777d_17 conda-forge
quippy 0.9.12 py38h6939b76_1 conda-forge
rdkit 2023.09.3 py38h6c71e64_1 conda-forge
readline 8.2 h8228510_1 conda-forge
referencing 0.32.0 pyhd8ed1ab_0 conda-forge
reportlab 4.0.8 py38h01eb140_0 conda-forge
requests 2.31.0 pyhd8ed1ab_0 conda-forge
rfc3339-validator 0.1.4 pyhd8ed1ab_0 conda-forge
rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge
rhash 1.4.4 hd590300_0 conda-forge
rlpycairo 0.2.0 pyhd8ed1ab_0 conda-forge
rpds-py 0.13.2 py38h0cc4f7c_0 conda-forge
ruamel.yaml 0.18.5 py38h01eb140_0 conda-forge
ruamel.yaml.clib 0.2.7 py38h01eb140_2 conda-forge
s3transfer 0.9.0 pyhd8ed1ab_0 conda-forge
scikit-learn 1.3.2 py38ha25d942_2 conda-forge
scipy 1.10.1 py38h59b608b_3 conda-forge
sdaxen_python_utilities 0.1.5 pyhd8ed1ab_0 conda-forge
send2trash 1.8.2 pyh41d4057_0 conda-forge
sentinels 1.0.0 py_1 conda-forge
setuptools 68.2.2 pyhd8ed1ab_0 conda-forge
sip 6.7.12 py38h17151c0_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sleef 3.5.1 h9b69904_2 conda-forge
smart_open 6.4.0 pyhd8ed1ab_0 conda-forge
snappy 1.1.10 h9fff704_0 conda-forge
sniffio 1.3.0 pyhd8ed1ab_0 conda-forge
soupsieve 2.5 pyhd8ed1ab_1 conda-forge
spglib 2.2.0 py38hb851694_1 conda-forge
sqlalchemy 2.0.23 py38h01eb140_0 conda-forge
sshtunnel 0.4.0 pyhd8ed1ab_1 conda-forge
stack_data 0.6.2 pyhd8ed1ab_0 conda-forge
starlette 0.27.0 pyhd8ed1ab_0 conda-forge
sympy 1.12 pypyh9d50eac_103 conda-forge
sysroot_linux-64 2.12 he073ed8_16 conda-forge
tabulate 0.9.0 pyhd8ed1ab_1 conda-forge
tbb 2021.11.0 h00ab1b0_0 conda-forge
tenacity 8.2.3 pyhd8ed1ab_0 conda-forge
terminado 0.18.0 pyh0d859eb_0 conda-forge
threadpoolctl 3.2.0 pyha21a80b_0 conda-forge
tinycss2 1.2.1 pyhd8ed1ab_0 conda-forge
tk 8.6.13 noxft_h4845f30_101 conda-forge
toml 0.10.2 pyhd8ed1ab_0 conda-forge
tomli 2.0.1 pyhd8ed1ab_0 conda-forge
tomlkit 0.12.3 pyha770c72_0 conda-forge
toolz 0.12.0 pyhd8ed1ab_0 conda-forge
tornado 6.3.3 py38h01eb140_1 conda-forge
tqdm 4.66.1 pyhd8ed1ab_0 conda-forge
traitlets 5.14.0 pyhd8ed1ab_0 conda-forge
types-python-dateutil 2.8.19.14 pyhd8ed1ab_0 conda-forge
typing-extensions 4.9.0 hd8ed1ab_0 conda-forge
typing_extensions 4.9.0 pyha770c72_0 conda-forge
typing_utils 0.1.0 pyhd8ed1ab_0 conda-forge
ukkonen 1.0.1 py38h7f3f72f_4 conda-forge
uncertainties 3.1.7 pyhd8ed1ab_0 conda-forge
unicodedata2 15.1.0 py38h01eb140_0 conda-forge
uri-template 1.3.0 pyhd8ed1ab_0 conda-forge
urllib3 1.26.18 pyhd8ed1ab_0 conda-forge
virtualenv 20.25.0 pyhd8ed1ab_0 conda-forge
voro 0.4.6 h00ab1b0_0 conda-forge
wcwidth 0.2.12 pyhd8ed1ab_0 conda-forge
webcolors 1.13 pyhd8ed1ab_0 conda-forge
webencodings 0.5.1 pyhd8ed1ab_2 conda-forge
websocket-client 1.7.0 pyhd8ed1ab_0 conda-forge
werkzeug 3.0.1 pyhd8ed1ab_0 conda-forge
wget 1.20.3 ha35d2d1_1 conda-forge
wheel 0.42.0 pyhd8ed1ab_0 conda-forge
xarray 2022.11.0 py38h06a4308_0
xarray-einstats 0.5.1 pyhd8ed1ab_0 conda-forge
xcb-util 0.4.0 hd590300_1 conda-forge
xcb-util-image 0.4.0 h8ee46fc_1 conda-forge
xcb-util-keysyms 0.4.0 h8ee46fc_1 conda-forge
xcb-util-renderutil 0.3.9 hd590300_1 conda-forge
xcb-util-wm 0.4.1 h8ee46fc_1 conda-forge
xkeyboard-config 2.40 hd590300_0 conda-forge
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.1.1 hd590300_0 conda-forge
xorg-libsm 1.2.4 h7391055_0 conda-forge
xorg-libx11 1.8.7 h8ee46fc_0 conda-forge
xorg-libxau 1.0.11 hd590300_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h0b41bf4_2 conda-forge
xorg-libxrender 0.9.11 hd590300_0 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge
xorg-xf86vidmodeproto 2.3.1 h7f98852_1002 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
zeromq 4.3.5 h59595ed_0 conda-forge
zipp 3.17.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hd590300_5 conda-forge
zstd 1.5.5 hfc55251_0 conda-forge
ERROR: Directory '.' is not installable. Neither 'setup.py' nor 'pyproject.toml' found.
/home/top/VENV/conda_surface_sampling/lib/python3.8/site-packages
An error occurred: cannot import name 'NeuralFF' from 'nff' (/home/top/miniconda3/envs/nff/lib/python3.9/site-packages/nff/init.py)
Traceback (most recent call last):
File "/tmp/ipykernel_776040/2079156642.py", line 22, in
do I have to run the training for these modules to appear?
Hi, you won't have to train a model to use NFF. What command are you trying to run when this error popped up?
Is it pip install .
in the NeuralForceField
folder?
%xmode Verbose
import traceback import sys
sys.path.append('/home/top/miniconda3/envs/nff/lib/python3.9/site-packages/') from nff import nn # Import the nn submodule
print(dir(nn))
print(sys.version)
!conda activate nff
!conda init
!conda list
!pip install .
!echo $PYTHONPATH
try:
import torch
from nff import NeuralFF, AtomsBatch, EnsembleNFF
I see. It seems like you're running a Jupyter notebook that you created. If possible, I would suggest you modify /.bashrc
as documented in the Setup section of the README, specifically
export NFFDIR="/path/to/NeuralForceField"
export PYTHONPATH=$NFFDIR:$PYTHONPATH
You might have to restart the terminal.
After that, try to run tutorials/SrTiO3_001.ipynb
.
NFFDIR is the path to the install directory or the conda virtual environment? thanks
It's the path to the install directory or the main folder.
Say if I clone the NeuralForceField repo on my home directory and my home directory is /home/usr
, the path should be /home/usr/NeuralForceField
.
ok here is more information, I just previously posted the diagnostics code from the notebook to show that those three custom python modules are not in NFF
Here is more, hope it isnt confusing
ON debian 12
MORE INFORMATION ~/.bashrc function set_default_paths() {
export PATH="/usr/bin:/usr/sbin:/usr/lib/cuda/bin:/home/top/.cargo/bin:/usr/local/bin:/bin:/usr/local/games:/usr/games"
# Clear PYTHONPATH
export PYTHONPATH="/usr/bin/python3"
# Deactivate conda if active and not in a legitimate path
if [[ -n "$CONDA_PREFIX" && "$PWD" != "/sda1/NeuralForceField"* ]]; then
conda deactivate
fi
# Deactivate venv if active and not in a legitimate path
if [[ -n "$VIRTUAL_ENV" && "$PWD" != "/sda1/lammps"* ]]; then
deactivate
fi
# Deactivate venv if active and not in a legitimate path
if [[ -n "$VIRTUAL_ENV" && "$PWD" != "/sda1/surface-sampling"* ]]; then
deactivate
fi
}
function update_environment() { if [[ "$PWD" == "/sda1/NeuralForceField"* ]]; then set_default_paths export PATH="/sda1/NeuralForceField:$PATH"
export PYTHONPATH="/home/top/miniconda3/envs/nff/lib/python3.9/site-packages:$NFFDIR"
elif [[ "$PWD" == "/sda1/surface-sampling"* ]]; then
set_default_paths
export PATH="/sda1/surface_sampling:$PATH"
export PYTHONPATH="/home/top/VENV/conda_surface_sampling/lib/python3.8/site-packages"
elif [[ "$PWD" == "/sda1/lammps"* ]]; then
set_default_paths
export PATH="/sda1/lammps/src/:$PATH"
export PYTHONPATH="/home/top/VENV/lammps/bin/python"
else
set_default_paths
fi
}
export LD_LIBRARY_PATH="/usr/local/ssl/lib:/usr/local/cuda/lib64" export EDITOR="kate"
export MPI_HOME="/usr/lib/x86_64-linux-gnu/openmpi" export PATH="$MPI_HOME/bin:$PATH" export LD_LIBRARY_PATH="$MPI_HOME/lib:$LD_LIBRARY_PATH" export C_INCLUDE_PATH="$MPI_HOME/include" export CPLUS_INCLUDE_PATH="$MPI_HOME/include"
export NFFDIR="/sda1/NeuralForceField"
PROMPT_COMMAND="update_environment"
python3() { REAL_PYTHON3="/usr/bin/python3" SCRIPT_PATH="~/add_required_lines.sh"
script_file="$1"
if [[ -f "$script_file" && -r "$script_file" ]]; then
if head -n 5 "$script_file" | grep -q 'python'; then
bash "$SCRIPT_PATH" "$script_file"
fi
fi
exec "$REAL_PYTHON3" "$@"
}
conda_setup="$('/home/top/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)" if [ $? -eq 0 ]; then eval "$__conda_setup" else if [ -f "/home/top/miniconda3/etc/profile.d/conda.sh" ]; then . "/home/top/miniconda3/etc/profile.d/conda.sh" else export PATH="/home/top/miniconda3/bin:$PATH" fi fi unset conda_setup
------------------------ NOTEBOOK AS TEXT------------------------
{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import os\n",
"import pickle\n",
"import sys\n",
"sys.path.append('/home/top/miniconda3/envs/nff/lib/python3.9/site-packages')\n",
"import sys\n",
"sys.path.append('/home/top/miniconda3/envs/nff/lib/python3.9')\n",
"import nff\n",
"from collections.abc import MutableMapping\n",
"\n",
"import pymc\n",
"from time import perf_counter\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using GPU 0\n"
]
}
],
"source": [
"import torch\n",
"\n",
"#Check GPU availability\n",
"if torch.cuda.is_available():\n",
" # Get the index of the current GPU\n",
" gpu_index = torch.cuda.current_device()\n",
" print(f\"Using GPU {gpu_index}\")\n",
"else:\n",
" print(\"CUDA is not available.\")\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"import os\n",
"import pickle\n",
"import sys\n",
"\n",
"# uncomment and replace with correct path if there are import errors\n",
"# sys.path.append(\"/sda1/surface-sampling/\")\n",
"# sys.path.append(\"/sda1/NeuralForceField/\")\n",
"# os.environ[\"LAMMPS_POTENTIALS\"] = \"/sda1/lammps/potentials/\"\n",
"# os.environ[\"LAMMPS_COMMAND\"] =\"/sda1/lammps/src/lmp_mpi\"\n",
"# os.environ[\"ASE_LAMMPSRUN_COMMAND\"] = os.environ[\"LAMMPS_COMMAND\"]\n",
"\n",
"from mcmc import MCMC\n",
"from time import perf_counter"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialize test slab and parameters"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Load prepared pristine slab\n",
"adsorbates = [\"Sr\", \"Ti\", \"O\"]\n",
"chem_pot = [-2, 0, 0] # in eV, 1 entry for each chempot\n",
"\n",
"element = []\n",
"slab_pkl = open(\"data/SrTiO3_001_2x2_pristine_slab.pkl\", \"rb\")\n",
"slab = pickle.load(slab_pkl)\n",
"\n",
"surface_name = \"SrTiO3(001)\"\n",
"alpha = 1 # no annealing\n",
"temp = 1.0 # in terms of kbT\n",
"num_sweeps = 10 # can adjust"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Obtain adsorption sites"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pymatgen.analysis.adsorption import AdsorbateSiteFinder\n",
"from pymatgen.io.ase import AseAtomsAdaptor\n",
"\n",
"pristine_slab = slab.copy()\n",
"pristine_pmg_slab = AseAtomsAdaptor.get_structure(pristine_slab)\n",
"site_finder = AdsorbateSiteFinder(pristine_pmg_slab)\n",
"sites = site_finder.find_adsorption_sites(\n",
" distance=1.55, put_inside=False, symm_reduce=False, no_obtuse_hollow=False\n",
")\n",
"ads_positions = sites[\"all\"]\n",
"print(\"adsorption coordinates are\")\n",
"print(ads_positions)\n",
"\n",
"sweep_size = len(ads_positions)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up NFF (calculator). We are using neural network weights from our Zenodo dataset (https://zenodo.org/record/7927039). The ensemble requires an offset_data.json
file"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exception reporting mode: Verbose\n",
"['AttentionPool', 'BatchedPreActivation', 'BondPrior', 'ChemPropConv', 'ChemPropInit', 'ChemPropMsgToNode', 'ConfAttention', 'CosineEnvelope', 'CpSchNetConv', 'DEFAULT_BONDPRIOR_PARAM', 'DEFAULT_DROPOUT_RATE', 'DataLoader', 'Dense', 'DiabaticReadout', 'Diagonalize', 'DimeNet', 'DimeNetDelta', 'DimeNetDiabat', 'DimeNetDiabatDelta', 'DimeNetRadialBasis', 'DimeNetSphericalBasis', 'DirectedMessage', 'DirectedMessagePP', 'EPS', 'EPSILON', 'EdgeEmbedding', 'EdgeUpdateModule', 'EmbeddingBlock', 'Envelope', 'ExpNormalBasis', 'F', 'Gaussian', 'GaussianSmearing', 'GraphAttention', 'InteractionBlock', 'LeakyReLU', 'Linear', 'LinearConfAttention', 'MeanPool', 'MessagePassingModule', 'MixedSchNetConv', 'ModuleDict', 'MolFpPool', 'NodeEmbedding', 'NodeMultiTaskReadOut', 'OutputBlock', 'PainnRadialBasis', 'PreActivation', 'RadialBasis', 'ReLU', 'ResidualBlock', 'ScaleShift', 'SchNet', 'SchNetConv', 'SchNetDiabat', 'SchNetEdgeFilter', 'SchNetEdgeUpdate', 'Sequential', 'Softmax', 'SphericalBasis', 'StochasticIncrease', 'SumPool', 'TestModules', 'builtins', 'cached', 'doc', 'file', 'loader', 'name', 'package', 'path', 'spec', 'activations', 'add_stress', 'adj_nbrs_and_z', 'att_readout_probs', 'batch_and_sum', 'bessel_basis', 'chemprop_msg_to_node', 'chemprop_msg_update', 'clean_matrix', 'collections', 'compute_angle', 'compute_grad', 'compute_hess', 'computejacobian', 'constant', 'construct_module_dict', 'construct_sequential', 'copy', 'diabat', 'dimenet', 'gaussian_smearing', 'general_batched_hessian', 'get_act', 'get_default_readout', 'get_dense', 'get_offsets', 'get_painn_hessians', 'get_rij', 'get_schnet_hessians', 'grad', 'graphconv', 'graphop', 'hess_from_atoms', 'hess_from_pad', 'hess_from_results', 'inspect', 'lattice_points_in_supercell', 'layer_types', 'layers', 'models', 'modules', 'nn', 'np', 'pad', 'partial', 'real_sph_harm', 'remove_bias', 'results_from_stack', 'scatter_add', 'schnet', 'schnet_batched_hessians', 'shifted_softplus', 'single_spec_nbrs', 'softmax', 'sum_and_grad', 'sym', 'tensorgrad', 'to_module', 'torch', 'torch_nbr_list', 'unittest', 'utils', 'xavieruniform', 'zeros_initializer']\n",
"3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) \n",
"[GCC 12.3.0]\n",
"\n",
"CondaError: Run 'conda init' before 'conda activate'\n",
"\n",
"no change /home/top/miniconda3/condabin/conda\n",
"no change /home/top/miniconda3/bin/conda\n",
"no change /home/top/miniconda3/bin/conda-env\n",
"no change /home/top/miniconda3/bin/activate\n",
"no change /home/top/miniconda3/bin/deactivate\n",
"no change /home/top/miniconda3/etc/profile.d/conda.sh\n",
"no change /home/top/miniconda3/etc/fish/conf.d/conda.fish\n",
"no change /home/top/miniconda3/shell/condabin/Conda.psm1\n",
"no change /home/top/miniconda3/shell/condabin/conda-hook.ps1\n",
"no change /home/top/miniconda3/lib/python3.11/site-packages/xontrib/conda.xsh\n",
"no change /home/top/miniconda3/etc/profile.d/conda.csh\n",
"no change /home/top/.bashrc\n",
"No action taken.\n",
"# packages in environment at /home/top/VENV/conda_surface_sampling:\n",
"#\n",
"# Name Version Build Channel\n",
"_libgcc_mutex 0.1 conda_forge conda-forge\n",
"_openmp_mutex 4.5 2_kmp_llvm conda-forge\n",
"aioitertools 0.11.0 pyhd8ed1ab_0 conda-forge\n",
"alsa-lib 1.2.10 hd590300_0 conda-forge\n",
"annotated-types 0.6.0 pyhd8ed1ab_0 conda-forge\n",
"anyio 4.1.0 pyhd8ed1ab_0 conda-forge\n",
"argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge\n",
"argon2-cffi-bindings 21.2.0 py38h01eb140_4 conda-forge\n",
"arrow 1.3.0 pyhd8ed1ab_0 conda-forge\n",
"arviz 0.15.1 pyhd8ed1ab_0 conda-forge\n",
"ase 3.22.1 pyhd8ed1ab_1 conda-forge\n",
"astroid 3.0.2 py38h578d9bd_0 conda-forge\n",
"asttokens 2.4.1 pyhd8ed1ab_0 conda-forge\n",
"async-lru 2.0.4 pyhd8ed1ab_0 conda-forge\n",
"attr 2.5.1 h166bdaf_1 conda-forge\n",
"attrs 23.1.0 pyh71513ae_1 conda-forge\n",
"babel 2.14.0 pyhd8ed1ab_0 conda-forge\n",
"backcall 0.2.0 pyh9f0ad1d_0 conda-forge\n",
"bcrypt 4.1.1 py38h0cc4f7c_0 conda-forge\n",
"beautifulsoup4 4.12.2 pyha770c72_0 conda-forge\n",
"binutils 2.40 hdd6e379_0 conda-forge\n",
"binutils_impl_linux-64 2.40 hf600244_0 conda-forge\n",
"binutils_linux-64 2.40 hbdbef99_2 conda-forge\n",
"blas 2.120 openblas conda-forge\n",
"blas-devel 3.9.0 20_linux64_openblas conda-forge\n",
"bleach 6.1.0 pyhd8ed1ab_0 conda-forge\n",
"blinker 1.7.0 pyhd8ed1ab_0 conda-forge\n",
"blosc 1.21.5 h0f2a231_0 conda-forge\n",
"boto3 1.34.0 pyhd8ed1ab_0 conda-forge\n",
"botocore 1.34.0 pyhd8ed1ab_0 conda-forge\n",
"brotli 1.1.0 hd590300_1 conda-forge\n",
"brotli-bin 1.1.0 hd590300_1 conda-forge\n",
"brotli-python 1.1.0 py38h17151c0_1 conda-forge\n",
"bzip2 1.0.8 hd590300_5 conda-forge\n",
"c-ares 1.23.0 hd590300_0 conda-forge\n",
"c-compiler 1.5.2 h0b41bf4_0 conda-forge\n",
"ca-certificates 2023.11.17 hbcca054_0 conda-forge\n",
"cached-property 1.5.2 hd8ed1ab_1 conda-forge\n",
"cached_property 1.5.2 pyha770c72_1 conda-forge\n",
"cachetools 5.3.2 pyhd8ed1ab_0 conda-forge\n",
"cairo 1.18.0 h3faef2a_0 conda-forge\n",
"catkit 0.5.4 pypi_0 pypi\n",
"certifi 2023.11.17 py38h06a4308_0 \n",
"cffi 1.16.0 py38h6d47a40_0 conda-forge\n",
"cfgv 3.3.1 pyhd8ed1ab_0 conda-forge\n",
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"reportlab 4.0.8 py38h01eb140_0 conda-forge\n",
"requests 2.31.0 pyhd8ed1ab_0 conda-forge\n",
"rfc3339-validator 0.1.4 pyhd8ed1ab_0 conda-forge\n",
"rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge\n",
"rhash 1.4.4 hd590300_0 conda-forge\n",
"rlpycairo 0.2.0 pyhd8ed1ab_0 conda-forge\n",
"rpds-py 0.13.2 py38h0cc4f7c_0 conda-forge\n",
"ruamel.yaml 0.18.5 py38h01eb140_0 conda-forge\n",
"ruamel.yaml.clib 0.2.7 py38h01eb140_2 conda-forge\n",
"s3transfer 0.9.0 pyhd8ed1ab_0 conda-forge\n",
"scikit-learn 1.3.2 py38ha25d942_2 conda-forge\n",
"scipy 1.10.1 py38h59b608b_3 conda-forge\n",
"sdaxen_python_utilities 0.1.5 pyhd8ed1ab_0 conda-forge\n",
"send2trash 1.8.2 pyh41d4057_0 conda-forge\n",
"sentinels 1.0.0 py_1 conda-forge\n",
"setuptools 68.2.2 pyhd8ed1ab_0 conda-forge\n",
"sip 6.7.12 py38h17151c0_0 conda-forge\n",
"six 1.16.0 pyh6c4a22f_0 conda-forge\n",
"sleef 3.5.1 h9b69904_2 conda-forge\n",
"smart_open 6.4.0 pyhd8ed1ab_0 conda-forge\n",
"snappy 1.1.10 h9fff704_0 conda-forge\n",
"sniffio 1.3.0 pyhd8ed1ab_0 conda-forge\n",
"soupsieve 2.5 pyhd8ed1ab_1 conda-forge\n",
"spglib 2.2.0 py38hb851694_1 conda-forge\n",
"sqlalchemy 2.0.23 py38h01eb140_0 conda-forge\n",
"sshtunnel 0.4.0 pyhd8ed1ab_1 conda-forge\n",
"stack_data 0.6.2 pyhd8ed1ab_0 conda-forge\n",
"starlette 0.27.0 pyhd8ed1ab_0 conda-forge\n",
"sympy 1.12 pypyh9d50eac_103 conda-forge\n",
"sysroot_linux-64 2.12 he073ed8_16 conda-forge\n",
"tabulate 0.9.0 pyhd8ed1ab_1 conda-forge\n",
"tbb 2021.11.0 h00ab1b0_0 conda-forge\n",
"tenacity 8.2.3 pyhd8ed1ab_0 conda-forge\n",
"terminado 0.18.0 pyh0d859eb_0 conda-forge\n",
"threadpoolctl 3.2.0 pyha21a80b_0 conda-forge\n",
"tinycss2 1.2.1 pyhd8ed1ab_0 conda-forge\n",
"tk 8.6.13 noxft_h4845f30_101 conda-forge\n",
"toml 0.10.2 pyhd8ed1ab_0 conda-forge\n",
"tomli 2.0.1 pyhd8ed1ab_0 conda-forge\n",
"tomlkit 0.12.3 pyha770c72_0 conda-forge\n",
"toolz 0.12.0 pyhd8ed1ab_0 conda-forge\n",
"tornado 6.3.3 py38h01eb140_1 conda-forge\n",
"tqdm 4.66.1 pyhd8ed1ab_0 conda-forge\n",
"traitlets 5.14.0 pyhd8ed1ab_0 conda-forge\n",
"types-python-dateutil 2.8.19.14 pyhd8ed1ab_0 conda-forge\n",
"typing-extensions 4.9.0 hd8ed1ab_0 conda-forge\n",
"typing_extensions 4.9.0 pyha770c72_0 conda-forge\n",
"typing_utils 0.1.0 pyhd8ed1ab_0 conda-forge\n",
"ukkonen 1.0.1 py38h7f3f72f_4 conda-forge\n",
"uncertainties 3.1.7 pyhd8ed1ab_0 conda-forge\n",
"unicodedata2 15.1.0 py38h01eb140_0 conda-forge\n",
"uri-template 1.3.0 pyhd8ed1ab_0 conda-forge\n",
"urllib3 1.26.18 pyhd8ed1ab_0 conda-forge\n",
"virtualenv 20.25.0 pyhd8ed1ab_0 conda-forge\n",
"voro 0.4.6 h00ab1b0_0 conda-forge\n",
"wcwidth 0.2.12 pyhd8ed1ab_0 conda-forge\n",
"webcolors 1.13 pyhd8ed1ab_0 conda-forge\n",
"webencodings 0.5.1 pyhd8ed1ab_2 conda-forge\n",
"websocket-client 1.7.0 pyhd8ed1ab_0 conda-forge\n",
"werkzeug 3.0.1 pyhd8ed1ab_0 conda-forge\n",
"wget 1.20.3 ha35d2d1_1 conda-forge\n",
"wheel 0.42.0 pyhd8ed1ab_0 conda-forge\n",
"xarray 2022.11.0 py38h06a4308_0 \n",
"xarray-einstats 0.5.1 pyhd8ed1ab_0 conda-forge\n",
"xcb-util 0.4.0 hd590300_1 conda-forge\n",
"xcb-util-image 0.4.0 h8ee46fc_1 conda-forge\n",
"xcb-util-keysyms 0.4.0 h8ee46fc_1 conda-forge\n",
"xcb-util-renderutil 0.3.9 hd590300_1 conda-forge\n",
"xcb-util-wm 0.4.1 h8ee46fc_1 conda-forge\n",
"xkeyboard-config 2.40 hd590300_0 conda-forge\n",
"xorg-kbproto 1.0.7 h7f98852_1002 conda-forge\n",
"xorg-libice 1.1.1 hd590300_0 conda-forge\n",
"xorg-libsm 1.2.4 h7391055_0 conda-forge\n",
"xorg-libx11 1.8.7 h8ee46fc_0 conda-forge\n",
"xorg-libxau 1.0.11 hd590300_0 conda-forge\n",
"xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge\n",
"xorg-libxext 1.3.4 h0b41bf4_2 conda-forge\n",
"xorg-libxrender 0.9.11 hd590300_0 conda-forge\n",
"xorg-renderproto 0.11.1 h7f98852_1002 conda-forge\n",
"xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge\n",
"xorg-xf86vidmodeproto 2.3.1 h7f98852_1002 conda-forge\n",
"xorg-xproto 7.0.31 h7f98852_1007 conda-forge\n",
"xz 5.2.6 h166bdaf_0 conda-forge\n",
"yaml 0.2.5 h7f98852_2 conda-forge\n",
"zeromq 4.3.5 h59595ed_0 conda-forge\n",
"zipp 3.17.0 pyhd8ed1ab_0 conda-forge\n",
"zlib 1.2.13 hd590300_5 conda-forge\n",
"zstd 1.5.5 hfc55251_0 conda-forge\n",
"\u001b[31mERROR: Directory '.' is not installable. Neither 'setup.py' nor 'pyproject.toml' found.\u001b[0m\u001b[31m\n",
"\u001b[0m/home/top/VENV/conda_surface_sampling/lib/python3.8/site-packages\n",
"An error occurred: cannot import name 'NeuralFF' from 'nff' (/home/top/miniconda3/envs/nff/lib/python3.9/site-packages/nff/init.py)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/tmp/ipykernel_776040/2079156642.py\", line 22, in offset_data.json
file\n",
" nnids = [\"model01\", \"model02\", \"model03\"]\n",
" model_dirs = [\n",
" os.path.join(\n",
" os.getcwd(),\n",
" \"data/nff\",\n",
" str(x),\n",
" \"best_model\",\n",
" )\n",
" for x in nnids\n",
" ]\n",
"\n",
" models = []\n",
" for modeldir in model_dirs:\n",
" m = NeuralFF.from_file(modeldir, device=DEVICE).model\n",
" models.append(m)\n",
"\n",
" nff_calc = EnsembleNFF(models, device=DEVICE)\n",
"\n",
" # set attributes\n",
" CUTOFF = 5\n",
" relax = True\n",
" relax_steps = 5\n",
" optimizer = \"BFGS\"\n",
" offset = True\n",
" offset_data = os.path.join(\n",
" os.getcwd(),\n",
" \"data/nff\",\n",
" \"offset_data.json\",\n",
" )\n",
"\n",
" slab_batch = AtomsBatch(\n",
" positions=slab.positions,\n",
" numbers=slab.numbers,\n",
" cell=slab.cell,\n",
" pbc=True,\n",
" cutoff=CUTOFF,\n",
" props={\"energy\": 0, \"energy_grad\": []},\n",
" calculator=nff_calc,\n",
" requires_large_offsets=True,\n",
" directed=True,\n",
" device=DEVICE,\n",
" )\n",
"\n",
" # fix bulk atoms\n",
" from ase.constraints import FixAtoms\n",
"\n",
" num_bulk_atoms = len(slab_batch)\n",
" bulk_indices = list(range(num_bulk_atoms))\n",
" print(f\"bulk indices {bulk_indices}\")\n",
" surf_indices = slab.get_surface_atoms()\n",
"\n",
" fix_indices = list(set(bulk_indices) - set(surf_indices))\n",
" print(f\"fix indices {fix_indices}\")\n",
"\n",
" c = FixAtoms(indices=fix_indices)\n",
" slab_batch.set_constraint(c)\n",
"except Exception as e:\n",
" print(f\"An error occurred: {e}\")\n",
" traceback.print_exc() "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform MCMC and view results. Detailed results can be found in the corresponding run in the SrTiO3(001)/
folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import copy\n",
"\n",
"run_slab = copy.deepcopy(slab_batch)\n",
"\n",
"mcmc = MCMC(\n",
" calc=nff_calc,\n",
" canonical=False,\n",
" testing=False,\n",
" surface_name=surface_name,\n",
" element=element,\n",
" adsorbates=adsorbates,\n",
" ads_coords=ads_positions,\n",
" relax=relax,\n",
" offset=offset,\n",
" offset_data=offset_data,\n",
" relax_steps=relax_steps,\n",
" optimizer=optimizer,\n",
")\n",
"\n",
"start = perf_counter()\n",
"# call the main function\n",
"mcmc.mcmc_run(\n",
" total_sweeps=num_sweeps,\n",
" sweep_size=sweep_size,\n",
" start_temp=temp,\n",
" pot=chem_pot, alpha=alpha, slab=run_slab\n",
")\n",
"\n",
"stop = perf_counter()\n",
"print(f\"Time taken = {stop - start} seconds\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Save structures for later use in latent space clustering or analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"structures = mcmc.history\n",
"with open(\"data/SrTiO3_001_2x2_mcmc_structures.pkl\", \"wb\") as f:\n",
" pickle.dump(structures, f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "surface_sampling_CPU",
"language": "python",
"name": "surface_sampling"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.18"
},
"vscode": {
"interpreter": {
"hash": "e3e0723b7fd9866ee8f9ae4f62931968cf8456ef2195b337b8930ae9f61708cf"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Hi, it seems like you're importing using this command from nff import NeuralFF, AtomsBatch, EnsembleNFF
. If you look at the ase.py here (https://github.com/learningmatter-mit/NeuralForceField/blob/master/nff/io/ase.py), you will find the NeuralFF, AtomsBatch, EnsembleNFF
classes. Can you try from nff.io.ase import NeuralFF, AtomsBatch, EnsembleNFF
or from nff.io import NeuralFF, AtomsBatch, EnsembleNFF
as in the tutorial notebook?
Also, if you want me to take a look at the notebook, can you upload the notebook as a file here (as a zip file)? Example: SrTiO3_001.ipynb.zip
Where can I get the NeuralFF python module and others that are missing from NFF?