This repository contains files for the ænet tutorial at the Psi-k workshop on Machine Learning Interatomic Potentials (ML-IP), Nov 15-19, 2021: https://www.mlip-workshop-2021.xyz
The atomic energy network (ænet, http://ann.atomistic.net) package is a software for the construction and usage of ML-IPs based on artificial neural networks (ANNs).
See also the following reference publications that describe some of the implementational aspects of ænet used in this tutorial.
Main ænet reference: N. Artrith and A. Urban, Comput. Mater. Sci. 114 (2016) 135-150.
Chebyshev descriptor: N. Artrith, A. Urban, and G. Ceder, Phys. Rev. B 96 (2017) 014112.
ænet-LAMMPS interface: M. S. Chen, T. Morawietz, H. Mori, T. E. Markland, and N. Artrith, J. Chem. Phys. 155 (2021) 074801.
The tutorial makes use of the atomic simulation environment (ASE) and LAMMPS.
ASE: A. H. Larsen et al., J. Phys.: Condens. Matter 29 (2017) 273002. and https://wiki.fysik.dtu.dk/ase/
LAMMPS: A. P. Thompson et al., Comp. Phys. Commun. 271 (2022) 10817 and https://www.lammps.org
A more in-depth tutorial introduction to ANN-based machine learning potentials can be found in:
See also: Strategies for the Construction of Neural-Network Based Machine-Learning Potentials (MLPs),
A.M. Miksch, T. Morawietz, J. Kästner, A. Urban, N. Artrith,
Mach. Learn.: Sci. Technol. 2 (2021) 031001.
See also: https://github.com/atomisticnet/MLP-beginners-guide
Contact: Nong Artrith ([email protected])
To learn more about ænet, sign up to the Google Group so that you don’t miss any announcements (e.g., for new releases) and can reach a wider community with any questions/issues related to ænet. Once subscribed, you can also post by sending emails to [email protected].
This directory contains an example that showcases the Chebyshev descriptor for local atomic environments proposed in Phys. Rev. B 96 (2017) 014112.
Owing for the large amount of data the reference data set and the
training set files are not included in the example. Otherwise the
example is self-contained. The reference data set used is the
TiO2 data set from Comput. Mater. Sci. 114 (2016)
135-150, which can
be downloaded from
ann.atomistic.net. Using that
data set, the training set file can be generated as described in the
first subdirectory 01-generate.
Output files generated at each step are contained in output
subdirectories.
Evaluation of the Chebyshev descriptor for all structures in the reference data set. The resulting feature vectors are written to a training set file.
generate.in: Input file forgenerate.xO.fingerprint.stpandTi.fingerprint.stp: Descriptor definitions for the atomic species O and Ti. For both species relatively small descriptor sizes with a radial expansion order of 16 and an angular order of 4 are used for set001 and set002. Another set (set003) uses different order of expansions: radial 22 and angular 6.
Training examples using the training set files generated in the
previous step (not included because of the file size). The results of
three training runs with different neural network sizes are shown in the
subdirectories set001, set002, and set003.
train.in: Input file fortrain.xget-energies: Subdirectory demonstrating how to write out the energies and errors of all training and testing samples (after training has completed).
Training will generate the ANN potential files O.15t-15t.nn and
Ti.15t-15t.nn which are also provided in the output subdirectory.
Usage of the ANN potentials trained in the previous step for the prediction of the entire reference data set.
predict.in: Input file forpredict.xO.15t-15t.nnandTi.15t-15t.nn: ANN potential files
The output (energies and atomic forces) can be found in the output
subdirectory.
Usage of the trained ANN potentials from set003 with external solftware.
Example for the Python (API) with the Atomistic Simulation Environment (ASE).
See the ænet Google Colab notebook.
O.40t-40t.nnandTi.40t-40t.nn: ANN potential files
Usage of the aenet-LAMMPS interface for molecular dynamics simulations.