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Materials research for fusion
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Cited by
Artificial intelligence-driven approaches for materials design and discovery
Cheng, M., Fu, C.-L., Okabe, R., Chotrattanapituk, A., Boonkird, A., Hung, N. T., & Li, M. (2026) · Nature Materials · doi:10.1038/s41563-025-02403-7 · Cited by 109
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Aurora, A., Devitre, A. R., Wylie, A. P. C., Rajagopal, J. A., & Short, M. P. (2025) · Nuclear Fusion · doi:10.1088/1741-4326/add74e · Cited by 1
“…safety and reliability under large cyclic and transient stresses typical of a FPP, (2) assessing these materials’ performance degradation, (3) making data-driven engineering design decisions, and (4) deriving high-level techno-economic metrics to maximize the commercial viability of fusion [6].”
Aurora, A., Devitre, A. R., Wylie, A. P., Rajagopal, J. A., & Short, M. P. (2025) · arXiv · doi:10.48550/arxiv.2506.17358
“…safety and reliability under large cyclic and transient stresses typical of a FPP, (2) assessing these materials’ performance degradation, (3) making data-driven engineering design decisions, and (4) deriving high-level techno-economic metrics to maximize the commercial viability of fusion [6].”
Metadata
- Authors
- Knaster, J., Moeslang, A., & Muroga, T.
- Venue
- Nature Physics
- Published
- 2016-05
- Volume
- 12
- Issue
- 5
- Pages
- 424-434
- ISSN
- 1745-2473 (ISSN-L)
- 1745-2481
- OpenAlex
- W2345583626
- Cited by
- 553
- References
- 33
- FWCI
- 18.34
- Type
- journal article
- Language
- en
- Open access
- green
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