pySSpredict: A python-based solid-solution strength prediction toolkit for complex concentrated alloys

doi:10.1016/j.commatsci.2022.111977

2023-03-01

Dongsheng Wen *

Michael S. Titus

DOI：10.1016/j.commatsci.2022.111977

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摘要

En 中文

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 pySSpredict, 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.

Keyword:

Solid-solution strengthening

Complex concentrated alloys

Yield strengths

Dislocations

High-throughput calculations

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期刊

Computational Materials Science

IF:

3.3

论文数:

1.3W

被引数:

3.6W

机构

Purdue University System

学者数:

3.9W

论文数: 3.6W

被引数: 66

引用论文

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation 评论: 材料项目: 加速材料创新的材料基因组方法

APL MATERIALS

IF4.5

2013-07-18

9.0K

OAAI

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