https://github.com/zhubonan/airsspy
Python bindings for the `buildcell` program for Ab Initio Random Structure Searching (AIRSS)
https://github.com/zhubonan/airsspy
airss materials-science structure-prediction
Last synced: about 2 months ago
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Python bindings for the `buildcell` program for Ab Initio Random Structure Searching (AIRSS)
- Host: GitHub
- URL: https://github.com/zhubonan/airsspy
- Owner: zhubonan
- License: gpl-2.0
- Created: 2020-04-06T14:03:50.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-31T09:12:31.000Z (about 1 year ago)
- Last Synced: 2025-03-26T21:11:18.934Z (3 months ago)
- Topics: airss, materials-science, structure-prediction
- Language: Python
- Homepage:
- Size: 382 KB
- Stars: 17
- Watchers: 1
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
airsspy
---------
[](https://mybinder.org/v2/gh/zhubonan/airsspy/HEAD)
A package to help working with the Ab initio Random Structure Searching ([AIRSS](https://www.mtg.msm.cam.ac.uk/Codes/AIRSS))
using Atomic simulation environment ([ase](https://wiki.fysik.dtu.dk/ase/)).
Supports building a search seed for AIRSS in a interactive python environments.
One of the important steps for performing a successful search with AIRSS is to have sensible seed for generating
random structures, which are subsequently relaxed using the method of choice.
In general, AIRSS only relies on a few simple parameters to generate random structure, such as numbers of atoms,
numbers of species and cell volume.
However, for complicated search involving surfaces and/or interfaces, hand-building seed files becomes a
tedious or impossible job to do.
ASE has a suite of tools for manipulate atomic structure which can be very helpful for building structures,
and here, for building search seeds.
AIRSS is a open source code licensed under GPLv2,
this package does not contain any source code of AIRSS nor links to it.
What this does
--------------
* Allow preparing seed for AIRSS using ASE's `atoms` interface
* Allow ase's calculators to be used in AIRSS to do relaxations
Try interactively
-----------------
Interactive jupyter notebook examples can be found in the `examples` folder.
Click the *binder* badge above to launch these examples in a pre-built environment and try it in your browser!
Dependences
-----------
* [ase](https://wiki.fysik.dtu.dk/ase/): The atomic simulation environment
* [castepinput](https://gitlab.com/bz1/castepinput): A light weight writer/reader for the input files of [CASTEP](www.caste.org).
Installation
-----------
The package can be installed from pypi together with the dependencies:
```
pip install airsspy
```
Alternative, one can also install directly from the repository (defaults to the master branch):
```
pip install git+https://github.com/zhubonan/airsspy
```
Usage
-----
Assuming you are familiar with `ase`, python and has some basic knowledge of AIRSS.
To prepare a seed for generating a *sensible* random structure:
```python
from airsspy import SeedAtoms
seed = SeedAtoms('C6')
seed.buiid.varvol = 20
seed.build.symmops = (2, 4)
# Can also access per `atom` tags/ketwords just like in ASE
for i in range(0, 6, 2):
atom = seed[i]
atom.tagname = 'CX'
atom.posamp = 2
```
To write the seed file onto the disk:
```python
atoms.write_seed('C6.cell')
# With IPython
# Use the buildcell executable to generate the file
!buildcell < C6.cell > C6-rand.cell
```
To generate a cell we can create a `Buildcell` instance,
which is helping wrapper to the `buildcell` program of AIRSS:
```python
from airsspy import Buildcell
buidcell = Buildcell(seed)
random_atoms = builcell.generate()
```
A shortcut is also available as an method of the `SeedAtoms`:
```python
random_atoms = seed.build_random_atoms()
```
Limitations
-----------
Due to the lack of `timeout` argument of `Popen.communicate` in python 2.7,
communication with the `buildcell` is not available. Hence, direct generation and
retrieval of the random structure are not supported in python. However, it is
still possible to write the seed out and call the program externally.