journal article Mar 21, 2024

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

View at Publisher Save 10.1021/acsami.3c17377
Topics

No keywords indexed for this article. Browse by subject →

References
63
[2]
Combining theory and experiment in electrocatalysis: Insights into materials design

Zhi Wei Seh, Jakob Kibsgaard, Colin F. Dickens et al.

Science 10.1126/science.aad4998
[3]
Opportunities and challenges for a sustainable energy future

Steven Chu, Arun Majumdar

Nature 10.1038/nature11475
[5]
Understanding of Oxygen Redox in the Oxygen Evolution Reaction

Xiaopeng Wang, Haoyin Zhong, Shibo Xi et al.

Advanced Materials 10.1002/adma.202107956
[6]
MOF-derived electrocatalysts for oxygen reduction, oxygen evolution and hydrogen evolution reactions

Hao-Fan Wang, Huan Pang, Stefan Kaskel et al.

Chemical Society Reviews 10.1039/c9cs00906j
[11]
Recent Advances in Design of Electrocatalysts for High‐Current‐Density Water Splitting

Yuting Luo, Manish Chhowalla, Bilu Liu

Advanced Materials 10.1002/adma.202108133
[14]
Nanoalloys:  From Theory to Applications of Alloy Clusters and Nanoparticles

Riccardo Ferrando, Julius Jellinek, Roy L. Johnston

Chemical Reviews 10.1021/cr040090g
[18]
Unraveling the electronegativity-dominated intermediate adsorption on high-entropy alloy electrocatalysts

Jiace Hao, Zechao Zhuang, Kecheng Cao et al.

Nature Communications 10.1038/s41467-022-30379-4
[29]
A transferable machine-learning scheme from pure metals to alloys for predicting adsorption energies

Xin Li, Bo Li, Ze Yang et al.

Journal of Materials Chemistry A 10.1039/d1ta09184k
[31]
Neural Network-Assisted Development of High-Entropy Alloy Catalysts: Decoupling Ligand and Coordination Effects

Zhuole Lu, Zhi Wen Chen, Chandra Veer Singh

Matter 10.1016/j.matt.2020.07.029
[43]
Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set

G. Kresse, J. Furthmüller

Physical Review B 10.1103/physrevb.54.11169
[44]
Projector augmented-wave method

P. E. Blöchl

Physical Review B 10.1103/physrevb.50.17953
[45]
Generalized Gradient Approximation Made Simple

John P. Perdew, Kieron Burke, Matthias Ernzerhof

Physical Review Letters 10.1103/physrevlett.77.3865
[46]
Semiempirical GGA‐type density functional constructed with a long‐range dispersion correction

Stefan Grimme

Journal of Computational Chemistry 10.1002/jcc.20495
[47]
The atomic simulation environment—a Python library for working with atoms

Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist et al.

Journal of Physics: Condensed Matter 10.1088/1361-648x/aa680e
[48]
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

Shyue Ping Ong, William Davidson Richards, Anubhav Jain et al.

Computational Materials Science 10.1016/j.commatsci.2012.10.028

Showing 50 of 63 references

Metrics
13
Citations
63
References
Details
Published
Mar 21, 2024
Vol/Issue
16(13)
Pages
16050-16061
License
View
Funding
National Natural Science Foundation of China Award: 52305443
National Key Research and Development Program of China Award: 2021YFB3500403
Cite This Article
Chao Wang, Bing Wang, Zhipeng Chang, et al. (2024). Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys. ACS Applied Materials & Interfaces, 16(13), 16050-16061. https://doi.org/10.1021/acsami.3c17377