Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162051
Title: High-throughput method–accelerated design of Ni-based superalloys
Authors: Liu, Feng
Wang, Zexin
Wang, Zi
Zhong, Jing
Zhao, Lei
Jiang, Liang
Zhou, Runhua
Liu, Yong
Huang, Lan
Tan, Liming
Tian, Yujia
Zheng, Han
Fang, Qihong
Zhang, Lijun
Zhang, Lina
Wu, Hong
Bai, Lichun
Zhou, Kun
Keywords: Engineering::Materials
Issue Date: 2022
Source: Liu, F., Wang, Z., Wang, Z., Zhong, J., Zhao, L., Jiang, L., Zhou, R., Liu, Y., Huang, L., Tan, L., Tian, Y., Zheng, H., Fang, Q., Zhang, L., Zhang, L., Wu, H., Bai, L. & Zhou, K. (2022). High-throughput method–accelerated design of Ni-based superalloys. Advanced Functional Materials, 32(28), 2109367-. https://dx.doi.org/10.1002/adfm.202109367
Journal: Advanced Functional Materials
Abstract: Ever-increasing demands for superior alloys with improved high-temperature service properties require accurate design of their composition. However, conventional approaches to screen the properties of alloys such as creep resistance and microstructural stability cost a lot of time and resources. This work therefore proposes a novel high throughput–based design strategy for high-temperature alloys to accelerate their composition selections, by taking Ni-based superalloys as an example. A numerical inverse method is used to massively calculate the multielement diffusion coefficients based on an accurate atomic mobility database. These coefficients are subsequently employed to refine the physical models for tuning the creep rates and structural stability of alloys, followed by unsupervised machine learning to categorize their composition and determine the range of the composition with optimal performance. By using a strict screening criterion, two sets of composition with comprehensively optimal properties are selected, which is then validated by experiments. Compared with recent data-driven methods for materials design, this strategy exhibits high accuracy and efficiency attributed to the high-throughput multicomponent diffusion couples, self-developed atomic mobility database, and refined physical models. Since this strategy is independent of the alloy composition, it can efficiently accelerate the development of multicomponent high-performance alloys and tackle challenges in discovering novel materials.
URI: https://hdl.handle.net/10356/162051
ISSN: 1616-301X
DOI: 10.1002/adfm.202109367
Rights: © 2022 Wiley-VCH GmbH. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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