ULtrahigh TEmperature Refractory Alloys (ULTERA) Database of High Entropy Alloys
Authors/Creators
- 1. The Pennsylvania State University
- 2. University of Genoa, Italy
- 3. Penn State University
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4.
Pennsylvania State University
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5.
Virginia Tech
Description
ULTERA database, developed under the ARPA-E's ULTIMATE program, is aimed at collecting literature data on high entropy alloys (HEAs) to facilitate rapid ML-based discovery of new ones using forward and inverse design.
The main scope of this dataset is collecting data on compositionally complex alloys (CCAs), also known as high entropy alloys (HEAs) and multi-principle-element alloys (MPEAs), with extra attention given to (1) high-temperature (refractory) mechanical data, (2) phases present under different processing conditions. Although low-entropy alloys (incl. binaries) are typically not presented to the end-user (or counted in statistics), some are present and used in ML efforts; thus, all high-quality alloy data contributions are welcome! You can set up a contribution in as little as few minutes with this contribution repository at contribute.ultera.org
As of September 2024, ULTERA contains over:
- 7,900+ property-datapoints, corresponding to
- 3,000+ unique HEAs, collected from
- 570+ unique DOIs.
All data is available through a high-performance API, following FAIR principles, while statistics on it can be found at our ultera.org project web page. The database architecture is designed to automatically integrate starting literature data in real time with methods such as experiments, generative modeling, predictive modeling, and validations.
Beyond large size, ULTERA has further advantage of being highly curated with many steps of data validation and then processed through our abnormal data detection tools (pyqalloy.ultera.org).
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Currently, the database contents and form are being actively developed under a DOE ARPA-E ULTIMATE project, and data curation is performed to ensure high quality before releasing to the public. At the same time, we are open to sharing the data with active collaborators in a highly interactive fashion. Please get in touch with Adam Krajewski (ak@psu.edu) or Zi-Kui Liu (zxl15@psu.edu) for more information, making sure you provide details on (1) your background, (2) your use case for the dataset, and (3) what kind of feedback you can provide.
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Additional details
Related works
- Is described by
- Journal article: 10.20517/jmi.2021.05 (DOI)
- Journal article: 10.1557/s43578-023-01122-6 (DOI)
- Journal article: 10.1016/j.ijrmhm.2024.106673 (DOI)
- Journal article: 10.1016/j.msea.2024.147475 (DOI)
- Thesis: 10.48550/arXiv.2407.04648 (DOI)