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psi-k-mlip-workshop-2021

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Psi-k Machine-learning interatomic potential workshop 2021

psi-k-mlip-workshop-2021 최신버젼 다운로드

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# Psi-k MLIP Workshop 2021

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.](http://dx.doi.org/10.1016/j.commatsci.2015.11.047) 
Chebyshev descriptor: [N. Artrith, A. Urban, and G. Ceder, *Phys. Rev. B* **96** (2017) 014112.](http://dx.doi.org/10.1103/PhysRevB.96.014112) 
ænet-LAMMPS interface: [M. S. Chen, T. Morawietz, H. Mori, T. E. Markland, and N. Artrith, *J. Chem. Phys.* **155** (2021) 074801.](https://doi.org/10.1063/5.0063880)

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.](https://doi.org/10.1088/1361-648X/aa680e) and <https://wiki.fysik.dtu.dk/ase/> 
LAMMPS: [A. P. Thompson et al., *Comp. Phys. Commun.* **271** (2022) 10817](https://doi.org/10.1016/j.cpc.2021.108171) 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](https://doi.org/10.1088/2632-2153/abfd96).

See also: https://github.com/atomisticnet/MLP-beginners-guide

Contact: Nong Artrith (nartrith@atomistic.net)

To learn more about ænet, sign up to the [Google Group](https://groups.google.com/forum/#!forum/aenet)
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 aenet@googlegroups.com.

## Example of construction and application of ANN potentials

This directory contains an example that showcases the **Chebyshev
descriptor** for local atomic environments proposed in
[*Phys. Rev. B* **96** (2017)
014112](http://dx.doi.org/10.1103/PhysRevB.96.014112).

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](http://dx.doi.org/10.1016/j.commatsci.2015.11.047), which can
be downloaded from
[ann.atomistic.net](http://ann.atomistic.net/download/). 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.

01-generate
-----------

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 for `generate.x`
- `O.fingerprint.stp` and `Ti.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.

02-train
--------

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 for `train.x`
- `get-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.

03-predict
----------

Usage of the ANN potentials trained in the previous step for the
prediction of the entire reference data set.

- `predict.in`: Input file for `predict.x`
- `O.15t-15t.nn` and `Ti.15t-15t.nn`: ANN potential files

The output (energies and atomic forces) can be found in the `output`
subdirectory.

04-aenetLib-ann-md
----------

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.nn` and `Ti.40t-40t.nn`: ANN potential files

05-aenet-lammps-water
----------

Usage of the aenet-LAMMPS interface for molecular dynamics simulations.

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