Models of configurationally-complex alloys made simple

We present a Python package for the efficient generation of special quasi-random structures (SQS) for atomic-scale calculations of disordered systems. Both, a Monte-Carlo approach or a systematic enumeration of structures can be used to carry out optimizations to ensure the best optimal configuration is found for given cell size and composition. We present a measure of randomness based on Warren-Cowley short-range order parameters allowing for fast analysis of atomic structures. Hence, optimal structures are found in a reasonable time for several dozens or even hundreds of atoms. Both SQS optimizations and analysis of structures can be carried out via a command-line interface or a Python API. Additional features, such as optimization towards partial ordering or independent sublattices allow the generation of atomistic models of modern complex materials. Moreover, hybrid parallelization, as well as distribution of vacancies, are supported. The output data format is compatible with ase, pymatgen and pyiron packages to be easily embeddable in complex simulation workflows.

本报告推出一款Python软件包，旨在高效生成特殊准随机结构（SQS），以用于原子尺度上对无序系统的计算。该软件包支持采用蒙特卡洛方法或系统枚举结构来进行优化，以确保在给定的单元尺寸和组成下找到最佳配置。我们提出了一种基于Warren-Cowley短程有序参数的随机度度量方法，便于快速分析原子结构。因此，在合理的时间内，即可为数十甚至数百个原子找到最优结构。特殊准随机结构的优化以及结构的分析均可通过命令行界面或Python API完成。此外，针对部分有序或独立子晶格的优化功能，使得能够生成现代复杂材料的原子模型。同时，支持混合并行化以及空位分布。输出数据格式与ase、pymatgen和pyiron软件包兼容，便于轻松嵌入复杂的模拟工作流程。