Cargando…
Author Correction: Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks
Autores principales: | Pillai, Hemanth Somarajan, Li, Yi, Wang, Shih-Han, Omidvar, Noushin, Mu, Qingmin, Achenie, Luke E. K., Abild-Pedersen, Frank, Yang, Juan, Wu, Gang, Xin, Hongliang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941106/ https://www.ncbi.nlm.nih.gov/pubmed/36805611 http://dx.doi.org/10.1038/s41467-023-36746-z |
Ejemplares similares
-
Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks
por: Pillai, Hemanth Somarajan, et al.
Publicado: (2023) -
Infusing theory into deep learning for interpretable reactivity prediction
por: Wang, Shih-Han, et al.
Publicado: (2021) -
Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
por: Wang, Siwen, et al.
Publicado: (2020) -
Fabrication of
Trimetallic Fe–Co–Ni
Electrocatalysts for Highly Efficient Oxygen Evolution Reaction
por: Jung, Han Young, et al.
Publicado: (2022) -
Preparation of trimetallic electrocatalysts by one-step co-electrodeposition and efficient CO(2) reduction to ethylene
por: Jia, Shuaiqiang, et al.
Publicado: (2022)