Performance of the nitrogen reduction reaction on metal bound g-CN: a combined approach of machine learning and DFT
Developing a cost-effective and environmentally benign substitute for the energy-intensive Haber-Bosch process for the production of ammonia is a global challenge. The electrocatalytic nitrogen reduction reaction (NRR) under ambient conditions through the six proton-electron process has attracted si...
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Published in | Physical chemistry chemical physics : PCCP Vol. 24; no. 28; pp. 175 - 1758 |
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Main Authors | , , , , |
Format | Journal Article |
Published |
21.07.2022
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Online Access | Get full text |
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Summary: | Developing a cost-effective and environmentally benign substitute for the energy-intensive Haber-Bosch process for the production of ammonia is a global challenge. The electrocatalytic nitrogen reduction reaction (NRR) under ambient conditions through the six proton-electron process has attracted significant interest. Herein, a series of transition-metal (TM) based single atom catalysts (SAC) embedded on carbon nitride (C
6
N
6
) have been chosen to explore the NRR activity. The promising metals have been primarily screened through density functional theory (DFT) by calculating their adsorption energies on C
6
N
6
- energies for dinitrogen binding and the barriers at the rate determining step. Based on these criteria, amongst the 18 metal centers, Ta based C
6
N
6
emerges as a good candidate for the reduction of nitrogen to NH
3
. On the other hand, for the Machine Learning (ML) regression models, the covalent radius and the d-band center of the TM have been identified as the most correlated descriptors for predicting the adsorption energy of nitrogen on the active metal center. Besides, probabilistic modeling using the soft voting technique in the classification model allows us to predict the most efficient single atom catalyst. Despite the realistic bottleneck of having only a limited number of TMs to choose from, this technique effectively predicts the best catalyst from a modest dataset. With the highest probabilistic score, Ta based C
6
N
6
dominates over the other catalysts in a good agreement with DFT findings. This letter manifests the effectiveness of the soft voting technique in an ensemble-based classification model.
DFT calculations assisted by machine-learning models predict tantalum (Ta) to be a suitable single-atom catalyst (SAC) for the nitrogen reduction reaction (NRR). |
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Bibliography: | https://doi.org/10.1039/d2cp01901a Electronic supplementary information (ESI) available: List of hyperparameters of the regression model, adsorption energy of nitrogen in the end-on mode and side-on mode, feature statistics of the dataset considered for machine learning method, schematic diagram of work-flow for the classification model, cross validation result for different classification models. See DOI |
ISSN: | 1463-9076 1463-9084 |
DOI: | 10.1039/d2cp01901a |