Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning


Journal article


Dong Hyeon Mok, Hong Li, Guiru Zhang, Chaehyeon Lee, Kun Jiang, Seoin Back
Nature Communications, vol. 14, 2023, p. 7303


Cite

Cite

APA   Click to copy
Mok, D. H., Li, H., Zhang, G., Lee, C., Jiang, K., & Back, S. (2023). Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning. Nature Communications, 14, 7303. https://doi.org/10.1038/s41467-023-43118-0


Chicago/Turabian   Click to copy
Mok, Dong Hyeon, Hong Li, Guiru Zhang, Chaehyeon Lee, Kun Jiang, and Seoin Back. “Data-Driven Discovery of Electrocatalysts for CO2 Reduction Using Active Motifs-Based Machine Learning.” Nature Communications 14 (2023): 7303.


MLA   Click to copy
Mok, Dong Hyeon, et al. “Data-Driven Discovery of Electrocatalysts for CO2 Reduction Using Active Motifs-Based Machine Learning.” Nature Communications, vol. 14, 2023, p. 7303, doi:10.1038/s41467-023-43118-0.


BibTeX   Click to copy

@article{dong2023a,
  title = {Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning},
  year = {2023},
  journal = {Nature Communications},
  pages = {7303},
  volume = {14},
  doi = {10.1038/s41467-023-43118-0},
  author = {Mok, Dong Hyeon and Li, Hong and Zhang, Guiru and Lee, Chaehyeon and Jiang, Kun and Back, Seoin}
}


Share



Follow this website


You need to create an Owlstown account to follow this website.


Sign up

Already an Owlstown member?

Log in