An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning.

Xiao H, Li R, Shi X, Chen Y, Zhu L, Chen X, Wang L

DOI: 10.1038/s41467-023-42870-7
Published: 2023 Nov 2
Container: Nature communications
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41467-023-42870-7,
  title = {An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning.},
  author = {Xiao H and Li R and Shi X and Chen Y and Zhu L and Chen X and Wang L},
  year = {2023},
  journal = {Nature communications},
  doi = {10.1038/s41467-023-42870-7},
  url = {https://doi.org/10.1038/s41467-023-42870-7}
}

RIS

TY  - JOUR
TI  - An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning.
AU  - Xiao H
AU  - Li R
AU  - Shi X
AU  - Chen Y
AU  - Zhu L
AU  - Chen X
AU  - Wang L
PY  - 2023
JO  - Nature communications
DO  - 10.1038/s41467-023-42870-7
UR  - https://doi.org/10.1038/s41467-023-42870-7
ER  -

APA

H, X., R, L., X, S., Y, C., L, Z., X, C., & L, W. (2023). An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning.. Nature communications. https://doi.org/10.1038/s41467-023-42870-7

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pubmed · retrieved 2026-09-26T08:20:11.026Z