DOI: 10.5281/zenodo.18334456
title: Accompanying dataset for the paper: "AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials"

description: Contributions



Max Hodapp did contribute to the convertion of existing MLIP trainings into a Motoko workflow. This led to the Autopot repository.

Guillaume Anciaux did the code architecture conception of Motoko workflow manager, built upon Blackdynamite, a parametric study helper.


Funding sources



Max Hodapp gratefully acknowledges the financial support by the Austrian Federal Ministry for Labour and Economy and the National Foundation for Research, Technology and Development and the Christian Doppler Research Association.


Data structure and information

The directory data contains the resulting MLIP presented in the paper for MoTa and W cases, as well as the full execution result for the unary example which is part of the autopot package. In details:



data : workflow and results export supporting the paper



MoTa : MLIP produced by the workflow for Molybdenum-Tantalum alloy, as presented in paper



MoTa_elastic_constants.pdf : elastic constants report

MoTa_lattice_constants.pdf : lattice constants report

MoTa_lev16.mtp : configuration file for moment tensor potential

MoTa_stacking_fault_energies.pdf : stacking fault report

check_errors.ipynb : cheking validity of produced potential and produces figures



W : MLIP produced by the workflow for Tungsten, as presented in paper



W_dd_plot.pdf : differential displacement plot

W_energy_curve.pdf : minimum energy path for screw dislocation

W_lev16.mtp : configuration file for moment tensor potential

check_errors.ipynb : checking validity of produced potential and produces figures



unary : Full workflow execution of the unary autopot example



check_errors.ipynb : checking validity of produced potential and produces figures

create_configurations.ipynb : Creates the initial configuration set

init_training_cfgs.xyz : initial configurations used as initial training set

lammps.log : lammps log produced while checking errors (check_errors.ipynb execution)

md_cfgs.xyz : configurations for intitial md_select task

mlip.ini : produced by check_errors.ipynb

workflow : MLIP workflow full description and execution files



motoko.yaml : workflow metadata description

orchestrator.py : python script providing the orchestration

wf.zip : workflow database saved as a zip

eam : sp_calc Task, using eam classical MD runs



BD-sp_calc-runs.zip : all run produced files

bd.yaml : Metadata description for the task

bd.zip : task database saved as a zip

doIt.py : Actual execution script

launch.sh : Bash setup for environment (mlp, potential paths, ...)



md_select : MD selection Task



BD-md_select-runs.zip : all run produced

bd.yaml : Metadata description for the task

bd.zip : task database saved as a zip

doIt.py : Actual execution script

launch.sh : Bash setup for environment (mlp, potential paths, ...)



select : select Task, based on configuration grade



BD-select-runs.zip : all run produced

bd.yaml : Metadata description for the task

bd.zip : task database saved as a zip

doIt.py : Actual execution script

launch.sh : Bash setup for environment (mlp, potential paths, ...)



train : train Task, based on mlip-2



BD-train-runs.zip : all run produced

bd.yaml : Metadata description for the task

bd.zip : task database saved as a zip

doIt.py : Actual execution script

launch.sh : Bash setup for environment (mlp, potential paths, ...)









install-deps.sh : Script installing all necessary dependencies to execute the workflow (tested on debian)

pkg.txt : list of debian packages to install as dependencies

reproduce.sh : Clone autopot, install dependencies and launch the unary example

requirements.txt : Python package dependencies

ro-crate-metadata.json : Metadata extracted from the dataset