Ultrahigh TEmperature Refractory Alloys (ULTERA) Project
ULTERA Project, carried under the DOE ARPA-E ULTIMATE program, aims to develop a new generation of materials for turbine blades in gas turbines and related applications. As a part of it, we developed a new alloy data infrastructure populated by the largest database of high entropy alloys (HEAs), along with their experimental properties. As of June 2023, the database contains over 6,000 property data points of 2,500 HEAs coming from over 500 publications. Best to our knowledge, it is currently the largest database of HEAs in the world, and while not published at the moment, we welcome users who would like to use it in their research and provide feedback or use its robust infrastructure (see next paragraph) to merge their existing datasets with it.
ULTERA Database is not simply a dataset but features a robust set of data processing, curation, and aggregation tools we built for the last 3 years. These tools allowed us to remove around the 5-10% erroneous data we identified in datasets available in the literature, ranging from non-obvious duplicate data points resulting in underestimating test errors to systematic composition parsing errors preventing model convergence or throwing off extrapolation capabilities. Most of our tools are not published yet, as the project is ongoing (they give us a competitive advantage), but some have been published open-source as a Python package called PyQAlloy, which will eventually contain all of them. They will work on all datasets of alloys, ranging from complex, concentrated solutions, i.e. metallic glasses / High Entropy Alloys (HEAs) / Multi Principle Element Alloys (MPEAs) / Concentrated Complex Alloys (CCAs) to more traditional alloys such as steels, nickel-based superalloys, etc. It should also work very well for complex non-metallic systems, such as High Entropy Oxides (HEOs) and High Entropy Nitrides (HENs), and we would be interested in supporting such deployment.
On this website, you can find:
Statistics on the current state of the literature-extracted dataset (95% experimental). If you are interested in something, shoot an email to ak@psu.edu and we will add it.
Our team (as of Mar 2023)
Overview of our data infrastructure (see details on how it fits into our Generative deep learning as a tool for inverse design of high entropy refractory alloys doi.org/10.20517/jmi.2021.05)
Initial (2021) team presentations describing details of our approach in regard to (1) inverse design, (2) computational thermodynamics, and (3) experimental validations.
ULTERA HEA DATABASE STATISTICS
Our Team
Our Data Infrastructure
Project Introduction (TEAM PRESENTATION)
Computational Thermodynamics by Dr. Liu
Generative Modelling by Dr. Reinhart
Crystal Plasticity Modelling by Dr. Beese
Properties Relevant to ULTERAs by Dr. Shang
RHEAs by Lavanya