| Autor: |
D. Beniwal, P. Singh, S. Gupta, M. J. Kramer, D. D. Johnson, P. K. Ray |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
npj Computational Materials, Vol 8, Iss 1, Pp 1-11 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2057-3960 |
| DOI: |
10.1038/s41524-022-00842-3 |
| Popis: |
Abstract Despite a plethora of data being generated on the mechanical behavior of multi-principal element alloys, a systematic assessment remains inaccessible via Edisonian approaches. We approach this challenge by considering the specific case of alloy hardness, and present a machine-learning framework that captures the essential physical features contributing to hardness and allows high-throughput exploration of multi-dimensional compositional space. The model, tested on diverse datasets, was used to explore and successfully predict hardness in Al x Ti y (CrFeNi)1-x-y , Hf x Co y (CrFeNi)1-x-y and Al x (TiZrHf)1-x systems supported by data from density-functional theory predicted phase stability and ordering behavior. The experimental validation of hardness was done on TiZrHfAl x . The selected systems pose diverse challenges due to the presence of ordering and clustering pairs, as well as vacancy-stabilized novel structures. We also present a detailed model analysis that integrates local partial-dependencies with a compositional-stimulus and model-response study to derive material-specific insights from the decision-making process. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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