sqsgenerator 0.5.6

Web Application
📝 docs

sqsgenerator is a Python package, which allows you to efficiently generate optimised Special-Quasirandom-Structures (SQS). The package uses Warren-Cowley Short-Range-Order (SRO) parameters to quantify randomness. The core routines are written in C++ and directly usable with your browser

[!TIP] sqsgenerator runs natively (multithreaded) in your browser using WebAssembly. No installation is required, just open the 🚀WebApp and start using it.

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[!IMPORTANT] Version 0.4 brings breaking changes. Most of the parameter remain the same, but some have been renamed or removed. Please refer to the documentation for more details or open an issue in case you need help migrating your scripts.

Highlights

• 🚀 Blazingly fast short-range-order calculations (C++ core)
• ➰ Monte-Carlo and systematic approach to compute optimal atomic configuration
• 🧵multithreaded by default (optional MPI support) also in the browser 🌐
• 🔀 optimize multiple sublattices simultaneously in a single run
• 🔌 easy integration with other frameworks (ase, pymatgen and pyiron)

Installation

start directly

Start directly in your browser without any installation at 🌐 sqsgen.gehringer.tech.

You can preview the results, download single files or the entire optimization for further analysis on your local machine. The WebAssembly powered application is multithreaded and runs completely in your at near native speed.

via pip

You can install the latest release of sqsgenerator from PyPI using pip:

pip install sqsgenerator

using conda

The easiest way to install sqsgenerator is to use conda package manager. sqsgenerator is deployed on the conda-forge channel. To install use:

conda install -c conda-forge sqsgenerator

Native application (MPI)

Since version 0.4 a native application is available, which can be used in HPC environments. The application is MPI enabled and can be built from source. Please refer to the installation instructions for more details.

Cite us

In case you use the software in your research, please cite our article. Here is the BibTeX entry.

Documentation

• You can find the online documentation here
• Learn how to get started!
• For a more in-depth insight, you can read our research article