Issue 28, 2022

Performance of the nitrogen reduction reaction on metal bound g-C6N6: a combined approach of machine learning and DFT

Abstract

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 (C6N6) 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 C6N6 – energies for dinitrogen binding and the barriers at the rate determining step. Based on these criteria, amongst the 18 metal centers, Ta based C6N6 emerges as a good candidate for the reduction of nitrogen to NH3. 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 C6N6 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.

Graphical abstract: Performance of the nitrogen reduction reaction on metal bound g-C6N6: a combined approach of machine learning and DFT

Supplementary files

Article information

Article type
Paper
Submitted
26 ኤፕሪ 2022
Accepted
27 ጁን 2022
First published
27 ጁን 2022

Phys. Chem. Chem. Phys., 2022,24, 17050-17058

Performance of the nitrogen reduction reaction on metal bound g-C6N6: a combined approach of machine learning and DFT

M. Mukherjee, S. Dutta, M. Ghosh, P. Basuchowdhuri and A. Datta, Phys. Chem. Chem. Phys., 2022, 24, 17050 DOI: 10.1039/D2CP01901A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements