pySSpredict: A python-based solid-solution strength prediction toolkit for complex concentrated alloys
pySSpredict: A python-based solid-solution strength prediction toolkit for complex concentrated alloys
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DOI：
10.1016/j.commatsci.2022.111977
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发表时间：
2023-03
影响因子：
3.3
通讯作者：
Dongsheng Wen;M. Titus
中科院分区：
文献类型：
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作者：
Dongsheng Wen;M. Titus
固溶体高熵合金(HEAs)和复杂浓缩合金(CCAs)的出现为设计具有定制强度和塑性的新型合金提供了机会。越来越多的集成计算材料工程(ICME)社区可以从实施最先进的固溶体强化模型到合金设计实践中受益。本文介绍了P y S S P r e d i c t，这是一个基于巨蟒的开源工具包，它可以自动高通量地计算CCA的固溶强度和热力学性质。我们介绍了该程序的功能：(1)自动进行CCA的高通量强度计算，(2)管理数据库或其他软件中的热力学计算数据，以及(3)可视化和过滤数据以识别候选合金。该工具包实现了面心立方(FCC)的最新理论刃位错模型和体心立方(BCC)合金的刃位错/螺位错模型。PySSrecast代码托管在GitHub上，并部署在NanHUB上用于演示。
The emergence of solid solution high entropy alloys (HEAs) and complex concentrated alloys (CCAs) offers opportunities to design novel alloys with tailored strength and ductility. The growing community of integrated-computational materials engineering (ICME) can benefit from implementing state-of-the-art solid-solution strengthening models to alloy design practices. This paper introduces p y S S p r e d i c t, an open-source python-based toolkit that automates high-throughput calculations of solid-solution strengths of CCAs and thermodynamic properties. We present the functions of the pySSpredict code:(1) automating high-throughput calculations of strength for CCAs,(2) managing the data of thermodynamic calculations from databases or other software, and (3) visualizing and filtering the data to identify candidate alloys. The toolkit implements the latest theoretical edge dislocation model for face-centered cubic (FCC), and edge/screw dislocation models for body-centered cubic (BCC) alloys. The pySSpredict code is hosted on GitHub and deployed on nanoHUB for demonstrations.