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pace-tutorial

This repo contains files for tutorial on PACE and pacemaker

Tutorial slides here

Preparation

From dashboard (https://www.puhti.csc.fi/pun/sys/dashboard/)

  • Start Jupyter

    • Partition: interactive
    • Resources: Cores - 32 (best) / 16 (still OK)
    • Time: 4 hours
    • Settings: Python - tensorflow
    • Jupyter type - lab
    • Working directory: /user/[YOUR_USER_NAME]

  • Connect to Jupyter

Terminal in JupyterLab

  • New Laucnher (Ctrl+Shift+L) - Terminal

Installation of pacemaker (python-ace and tensorpotential)

echo 'export PYTHONPATH=$HOME/.local/lib/python3.9/site-packages/:$PYTHONPATH' >> ~/.bashrc 
source ~/.bashrc
cd
mkdir tools

python-ace

cd
cd tools
git clone https://github.com/ICAMS/python-ace.git
cd python-ace
pip install . --user
cd lib/maxvolpy
pip install . --user

tensorpotential

cd
cd tools 
git clone https://github.com/ICAMS/TensorPotential.git
cd TensorPotential
pip install . --user

LAMMPS

cd
cd tools/
git clone --depth 1  --branch develop https://github.com/lammps/lammps.git lammps
cd lammps/
mkdir build
cd build/
cmake -DCMAKE_BUILD_TYPE=Release -D BUILD_MPI=ON -DPKG_ML-PACE=ON  ../cmake
cmake --build . -- -j 
make install

Tutorial materials

cd
git clone https://github.com/yury-lysogorskiy/pace-tutorial.git
cd pace-tutorial
tar zxvf AlLi_vasp_data.tar.gz

TUTORIAL

DFT data collection

cd  AlLi_vasp_data
pace_collect --free-atom-energy auto --output-dataset-filename AlLi.pkl.gz

pacemaker: automatic input file generation

Prepare the folder and copy the dataset

cd
cd pace-tutorial
mkdir AlLi_fit
cd AlLi_fit
cp ../AlLi_vasp_data/AlLi.pkl.gz .

and run automatic input file generation

pacemaker -t

You will see following requests and should reply:

Generating 'input.yaml'
Enter training dataset filename (ex.: data.pckl.gzip, [TAB] – autocompletion): AlLi.pkl.gz
Enter test set fraction or size (ex.: 0.05 or [ENTER] - no test set): 0.05
Please enter list of elements (ex.: "Cu", "AlNi", [ENTER] - determine from dataset): [ENTER]
Enter number of functions per element ([ENTER] - default 700): 300
Enter cutoff (Angstrom, default:7.0): 6.0
Enter weighting scheme type - `uniform` or `energy` ([ENTER] - `uniform`): energy
Input file is written into `input.yaml`

pacemaker: run fit

If you have input.yaml file in the current folder, then just execute:

pacemaker

At the end you should find output_potential.yaml (if program terminates successfully) and interim_potential_0.yaml (always contains latest version of potential) You can rename one of these files into AlLi.yaml

mv interim_potential_0.yaml AlLi.yaml

pace_activeset: calculation of active set

In order to compute active set, run

pace_activeset -d fitting_data_info.pckl.gzip AlLi.yaml

LAMMPS: run MD with uncertainty indication

Go to AlLi-LAMMPS folder and execute LAMMPS there

cd
cd pace-tutorial/AlLi-LAMMPS
lmp -in in.lammps

You should see extrapolative_structures.dump file. If it is empty, then no extrapolation occurs. Also check c_max_pace_gamma column in log.lammps output. Try to increase supercell size, change temperature profile, switch to NPT, change atom types to get extrapolative structures. Remember, that LAMMPS will overwrite extrapolative_structures.dump file, so copy it beforehand.

pace_select: selection of structures for active learning

pace_select -p AlLi.yaml -a AlLi.asi -e "Al Li" -m 20 -o selected/POSCAR extrapolative_structures.dump

(optional) Active exploration

Check the Jupyter notebook in ~/tools/python-ace/examples/Active_Exploration.ipynb and adapt it for Al and/or Li and for your AlLi.yaml/asi potential

Further reading