Knowledge-informed graph attention networks enable defect-free alloy design for laser additive manufacturing.

Fu J, Yu H, Wang C, Wang L, Sun Z, Li J, van der Zwaag S, Raabe D, Xu W

DOI: 10.1038/s41467-026-77119-6
Published: 2026 Aug 26
Container: Nature communications
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s41467-026-77119-6,
  title = {Knowledge-informed graph attention networks enable defect-free alloy design for laser additive manufacturing.},
  author = {Fu J and Yu H and Wang C and Wang L and Sun Z and Li J and van der Zwaag S and Raabe D and Xu W},
  year = {2026},
  journal = {Nature communications},
  doi = {10.1038/s41467-026-77119-6},
  url = {https://doi.org/10.1038/s41467-026-77119-6}
}

RIS

TY  - JOUR
TI  - Knowledge-informed graph attention networks enable defect-free alloy design for laser additive manufacturing.
AU  - Fu J
AU  - Yu H
AU  - Wang C
AU  - Wang L
AU  - Sun Z
AU  - Li J
AU  - van der Zwaag S
AU  - Raabe D
AU  - Xu W
PY  - 2026
JO  - Nature communications
DO  - 10.1038/s41467-026-77119-6
UR  - https://doi.org/10.1038/s41467-026-77119-6
ER  -

APA

J, F., H, Y., C, W., L, W., Z, S., J, L., S, V. D. Z., D, R., & W, X. (2026). Knowledge-informed graph attention networks enable defect-free alloy design for laser additive manufacturing.. Nature communications. https://doi.org/10.1038/s41467-026-77119-6

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pubmed · retrieved 2026-09-26T06:08:53.952Z