Bibliographic Details
| Title: |
Unsupervised learning-aided extrapolation for accelerated design of superalloys |
| Authors: |
Weijie Liao, Ruihao Yuan, Xiangyi Xue, Jun Wang, Jinshan Li, Turab Lookman |
| Source: |
npj Computational Materials, Vol 10, Iss 1, Pp 1-8 (2024) |
| Publisher Information: |
Nature Portfolio, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Materials of engineering and construction. Mechanics of materials
LCC:Computer software |
| Subject Terms: |
Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765 |
| More Details: |
Abstract Machine learning has been widely used to guide the search for new materials by learning the patterns underlying available data. However, the pure prediction-oriented search is often biased to interpolation due to the limited data in a large unexplored space. Here we present a sampling framework towards extrapolation, that integrates unsupervised clustering, interpretable analysis, and similarity evaluation to sample target candidates with improved properties from a vast search space. Using the design of superalloys with improved $${\gamma }^{{\prime} }$$ γ ′ -phase solvus temperature ( $${T}_{{\gamma }^{{\prime} }}$$ T γ ′ ) as a model case, we start with sparse data, and by a few experiments, we find nine new superalloys with chemistries distinct to those in the training data. Three of them show improved $${T}_{{\gamma }^{{\prime} }}$$ T γ ′ by about 50 °C, a large enhancement for superalloys. Moreover, we find two features characterizing mismatch in atomic size and mixing enthalpy linearly effect. This work demonstrates the capability of unsupervised learning to search for new materials when limited data is available. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2057-3960 |
| Relation: |
https://doaj.org/toc/2057-3960 |
| DOI: |
10.1038/s41524-024-01358-8 |
| Access URL: |
https://doaj.org/article/e430ea3594124420a806b1ae5df34677 |
| Accession Number: |
edsdoj.430ea3594124420a806b1ae5df34677 |
| Database: |
Directory of Open Access Journals |
