Abstract
We report a curated dataset that brings together composition, processing conditions, microstructural details, and mechanical properties for 396 combinations of alloy composition and processing condition drawn from 100 peer-reviewed research articles on precipitate-containing multi-principal element alloys (MPEAs). The dataset was created by first utilizing a generative large language model for information extraction, followed by expert review to ensure accurate recovery of materials data. Compositional information was taken directly from tables and text, while processing routes — including homogenization, rolling, recrystallization, and aging — were converted into uniform temperature and time metrics. Microstructural descriptors, including precipitate phases and sizes, were consolidated into a consistent labeling scheme to accommodate the wide range of terminology used in published literature. Finally, mechanical property data, such as strength and ductility, were compiled together with the temperatures at which they were measured. These data provide a coherent view of the composition-processing-microstructure-property features explored in existing MPEA research and establish a resource that supports data-driven alloy design as well as future development of automated materials information-extraction methodologies. The complete dataset is available on Zenodo.
Recommended Citation
A. Raj et al., "A Dataset of Precipitate-containing Multi-principal Element Alloys," Data in Brief, vol. 65, article no. 112540, Elsevier, Apr 2026.
The definitive version is available at https://doi.org/10.1016/j.dib.2026.112540
Department(s)
Materials Science and Engineering
Publication Status
Open Access
Keywords and Phrases
Composition-processing-microstructure-property relationship; Materials science; Multi-principal element alloys; Precipitate
International Standard Serial Number (ISSN)
2352-3409
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2026 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Apr 2026
Comments
National Science Foundation, Grant DMREF-2522655