AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Nano Research Article
PDF (14.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Machine learning driven high-throughput screening of S and N-coordinated SACs for eNRR

Lintao Xu1, Yuhong Huang1,2 ( ), Haiping Lin1,2, Xiumei Wei1,2, Fei Ma3 ( )
School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710119, China
Shaanxi “Four Bodies and One Union” University-Enterprise Joint Research Center for Advanced Molybdenum-based Functional Materials, Xi’an 710119, China
State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an 710049, China
Show Author Information

Graphical Abstract

The machine learning (ML)-driven efficient screening of 196 transition metals (TM)@SxNy single-atom catalysts (SACs) is successfully conducted via 10 features and 4 ML models. The (Mo and W)@S3N1 exhibited remarkably limiting potential values of −0.26 and −0.25 V, respectively. A new descriptor and key factors, as well as their relationships, are explored to reveal the catalytic mechanism.

Abstract

This study constructs 196 transition metals (TM)@SxNy single-atom catalysts (SACs) (x = 0–4 and y = 0–4) and employs the eXtreme Gradient Boosting (XGBoost) classification model in machine learning (ML) for effectively distinguishing qualified and unqualified catalysts. The prediction accuracy rate is high, up to 95%. The SHapley Additive exPlanations (SHAP) analysis reveals that the N≡N bond length and the number of outermost d electrons (Nd) can well describe the nitrogen (N2) reduction reaction (NRR) activity. The relationships between N≡N, Nd, the adsorption energies of different intermediates ( ΔE∗N2, ΔE∗N2H, and ΔE∗NH2), the general descriptor (φ), and the Gibbs free energy of key steps ( ΔG∗N2, ΔG∗N2−∗N2H, and ΔG∗NH2−∗NH3) indicate that moderate nitrogen activation can enhance the reaction activity. Among the 17 screened SACs, Mo@S3N1, and W@S3N1 demonstrate the best catalytic performance, with limiting potential (UL) values of only −0.26 and −0.25 V under implicit solvation conditions. The electronic properties and variations in N≡N and TM–N bond lengths are investigated to reveal the origin of NRR activity. This study provides the decisive features and NRR dataset for ML research, as well as a feasible strategy for rational design of NRR SACs.

Keywords

nitrogen reduction reaction (NRR) processmachine learningcatalytic descriptorsSHapley Additive exPlanations (SHAP) analysis

Electronic Supplementary Material

Download File(s)
7289_ESM.pdf (4.8 MB)

References

[1]

Foster, S. L.; Bakovic, S. I. P.; Duda, R. D.; Maheshwari, S.; Milton, R. D.; Minteer, S. D.; Janik, M. J.; Renner, J. N.; Greenlee, L. F. Catalysts for nitrogen reduction to ammonia. Nat. Catal. 2018, 1, 490–500.
Crossref Google Scholar
[2]

Ren, Y. W.; Yu, C.; Tan, X. Y.; Huang, H. L.; Wei, Q. B.; Qiu, J. S. Strategies to suppress hydrogen evolution for highly selective electrocatalytic nitrogen reduction: Challenges and perspectives. Energy Environ. Sci. 2021, 14, 1176–1193.
Crossref Google Scholar
[3]

Zhao, X.; Hu, G. Z.; Chen, G. F.; Zhang, H. B.; Zhang, S. S.; Wang, H. H. Comprehensive understanding of the thriving ambient electrochemical nitrogen reduction reaction. Adv. Mater. 2021, 33, 2007650.
Crossref Google Scholar
[4]

Klerke, A.; Christensen, C. H.; Nørskov, J. K.; Vegge, T. Ammonia for hydrogen storage: Challenges and opportunities. J. Mater. Chem. 2008, 18, 2304–2310.
Crossref Google Scholar
[5]

Saadatjou, N.; Jafari, A.; Sahebdelfar, S. Ruthenium nanocatalysts for ammonia synthesis: A review. Chem. Eng. Commun. 2015, 202, 420–448.
Crossref Google Scholar
[6]

Mao, J. J.; Iocozzia, J.; Huang, J. Y.; Meng, K.; Lai, Y. K.; Lin, Z. Q. Graphene aerogels for efficient energy storage and conversion. Energy Environ. Sci. 2018, 11, 772–799.
Crossref Google Scholar
[7]

Kyriakou, V.; Garagounis, I.; Vasileiou, E.; Vourros, A.; Stoukides, M. Progress in the electrochemical synthesis of ammonia. Catal. Today 2017, 286, 2–13.
Crossref Google Scholar
[8]

Chen, G. F.; Ren, S. Y.; Zhang, L. L.; Cheng, H.; Luo, Y. R.; Zhu, K. H.; Ding, L. X.; Wang, H. H. Advances in electrocatalytic N2 reduction-strategies to tackle the selectivity challenge. Small Methods 2019, 3, 1800337.
Crossref Google Scholar
[9]

Yuan, S. F.; Ren, H.; Meng, G. D.; Zhao, W.; Zhu, H. Y.; Guo, W. Y. ZnO monolayer supported single-atom catalysts for efficient nitrogen electroreduction to ammonia. Appl. Surf. Sci. 2021, 555, 149682.
Crossref Google Scholar
[10]

Cui, X. Y.; Tang, C.; Zhang, Q. A review of electrocatalytic reduction of dinitrogen to ammonia under ambient conditions. Adv. Energy Mater. 2018, 8, 1800369.
Crossref Google Scholar
[11]

Légaré, M. A.; Bélanger-Chabot, G.; Dewhurst, R. D.; Welz, E.; Krummenacher, I.; Engels, B.; Braunschweig, H. Nitrogen fixation and reduction at boron. Science 2018, 359, 896–900.
Crossref Google Scholar
[12]

Erisman, J. W.; Sutton, M. A.; Galloway, J.; Klimont, Z.; Winiwarter, W. How a century of ammonia synthesis changed the world. Nat. Geosci. 2008, 1, 636–639.
Crossref Google Scholar
[13]

Chen, G. F.; Cao, X. R.; Wu, S. Q.; Zeng, X. Y.; Ding, L. X.; Zhu, M.; Wang, H. H. Ammonia electrosynthesis with high selectivity under ambient conditions via a Li+ incorporation strategy. J. Am. Chem. Soc. 2017, 139, 9771–9774.
Crossref Google Scholar
[14]

Gong, Y. T.; Li, H. C.; Wu, J. Z.; Song, X. Y.; Yang, X. Q.; Bao, X. B.; Han, X.; Kitano, M.; Wang, J. J.; Hosono, H. Unique catalytic mechanism for Ru-loaded ternary intermetallic electrides for ammonia synthesis. J. Am. Chem. Soc. 2022, 144, 8683–8692.
Crossref Google Scholar
[15]

Lv, X. S.; Wei, W.; Huang, B. B.; Dai, Y.; Frauenheim, T. High-throughput screening of synergistic transition metal dual-atom catalysts for efficient nitrogen fixation. Nano Lett. 2021, 21, 1871–1878.
Crossref Google Scholar
[16]

Hu, L.; Xing, Z.; Feng, X. F. Understanding the electrocatalytic interface for ambient ammonia synthesis. ACS Energy Lett. 2020, 5, 430–436.
Crossref Google Scholar
[17]

Wang, S. Y.; Shi, L.; Bai, X. W.; Li, Q.; Ling, C. Y.; Wang, J. L. Highly efficient photo-/electrocatalytic reduction of nitrogen into ammonia by dual-metal sites. ACS Cent. Sci. 2020, 6, 1762–1771.
Crossref Google Scholar
[18]

Lin, Y. X.; Feng, Y. Z.; Zhou, H.; Han, Y.; Sun, H.; Shi, L.; Meng, L. J.; Zhou, M.; Liu, Y. J.; Zhang, X. Y. Ligands dependent electrocatalytic nitrogen reduction performance in d–π conjugated molecules. Appl. Surf. Sci. 2022, 593, 153338.
Crossref Google Scholar
[19]

Lü, F.; Zhao, S. Z.; Guo, R. J.; He, J.; Peng, X. Y.; Bao, H. H.; Fu, J. T.; Han, L. L.; Qi, G. C.; Luo, J. et al. Nitrogen-coordinated single Fe sites for efficient electrocatalytic N2 fixation in neutral media. Nano Energy 2019, 61, 420–427.
Crossref Google Scholar
[20]

Long, X. H.; Huang, F.; Yao, Z. N.; Li, P.; Zhong, T.; Zhao, H. N.; Tian, S. H.; Shu, D.; He, C. Advancements in electrocatalytic nitrogen reduction: A comprehensive review of single-atom catalysts for sustainable ammonia synthesis. Small 2024, 20, 2400551.
Crossref Google Scholar
[21]

Han, L. L.; Hou, M. C.; Ou, P. F.; Cheng, H.; Ren, Z. H.; Liang, Z. X.; Boscoboinik, J. A.; Hunt, A.; Waluyo, I.; Zhang, S. S. et al. Local modulation of single-atomic Mn sites for enhanced ambient ammonia electrosynthesis. ACS Catal. 2021, 11, 509–516.
Crossref Google Scholar
[22]

Wang, M.; Huang, Y. H.; Ma, F.; Wei, X. M.; Hou, P. F.; Zhu, G. Q.; Du, R. H.; Zhang, J. M. Newly designed photocatalyst of Fe4 single clusters on g-C6N6 for nitrogen reduction reaction. Comput. Theor. Chem. 2023, 1222, 114074.
Crossref Google Scholar
[23]

Wang, M.; Huang, Y. H.; Ma, F.; Zhu, G. Q.; Zhang, J. M.; Wei, X. M.; Hou, P. F.; Du, R. H.; Liu, J. Theoretical insights into the mechanism of nitrogen-to-ammonia electroreduction on TM/g-C9N10. Mol. Catal. 2023, 547, 113391.
Crossref Google Scholar
[24]

Singh, A. R.; Rohr, B. A.; Statt, M. J.; Schwalbe, J. A.; Cargnello, M.; Nørskov, J. K. Strategies toward selective electrochemical ammonia synthesis. ACS Catal. 2019, 9, 8316–8324.
Crossref Google Scholar
[25]

Guo, X. Y.; Lin, S. R.; Gu, J. X.; Zhang, S. L.; Chen, Z. F.; Huang, S. P. Establishing a theoretical landscape for identifying basal plane active 2D metal borides (MBenes) toward nitrogen electroreduction. Adv. Funct. Mater. 2021, 31, 2008056.
Crossref Google Scholar
[26]

Sun, J. X.; Xia, P.; Lin, Y. X.; Zhang, Y. F.; Chen, A. J.; Shi, L.; Liu, Y. J.; Niu, X. H.; He, A. L.; Zhang, X. Y. Theoretical exploration of the nitrogen fixation mechanism of two-dimensional dual-metal TM1TM2@C9N4 electrocatalysts. Nanoscale Horiz. 2023, 8, 211–223.
Crossref Google Scholar
[27]

Cao, Y. Y.; Gao, Y. J.; Zhou, H.; Chen, X. L.; Hu, H.; Deng, S. W.; Zhong, X.; Zhuang, G. L.; Wang, J. G. Highly efficient ammonia synthesis electrocatalyst: Single Ru atom on naturally nanoporous carbon materials. Adv. Theory Simul. 2018, 1, 1800018.
Crossref Google Scholar
[28]

Tang, Y. A.; Chen, W. G.; Zhou, J. C.; Chai, H. D.; Li, Y.; Cui, Y. Q.; Feng, Z.; Dai, X. Q. Mechanistic insight into the selective catalytic oxidation for NO and CO on co-doping graphene sheet: A theoretical study. Fuel 2019, 253, 1531–1544.
Crossref Google Scholar
[29]

Tang, Y. N.; Chen, W. G.; Shen, Z. G.; Chang, S. S.; Zhao, M. Y.; Dai, X. Q. Nitrogen coordinated silicon-doped graphene as a potential alternative metal-free catalyst for CO oxidation. Carbon 2017, 111, 448–458.
Crossref Google Scholar
[30]

Wang, X.; Wang, J.; Wang, D. L.; Dou, S. O.; Ma, Z. L.; Wu, J. H.; Tao, L.; Shen, A. L.; Ouyang, C. B.; Liu, Q. H. et al. One-pot synthesis of nitrogen and sulfur co-doped graphene as efficient metal-free electrocatalysts for the oxygen reduction reaction. Chem. Commun. 2014, 50, 4839–4842.
Crossref Google Scholar
[31]

Wang, M.; Huang, Y. H.; Lin, H. P.; Zhao, H. L.; Ma, F.; Zhang, J. M.; Wei, X. M. The enhanced electro/photocatalytic activity for nitric oxide reduction to ammonia by B@g-C9N10 monolayer. Mater. Chem. Phys. 2024, 315, 128914.
Crossref Google Scholar
[32]

Zafari, M.; Kumar, D.; Umer, M.; Kim, K. S. Machine learning-based high throughput screening for nitrogen fixation on boron-doped single-atom catalysts. J. Mater. Chem. A. 2020, 8, 5209–5216.
Crossref Google Scholar
[33]

Tian, Y.; Xu, D. Z.; Chu, K.; Wei, Z.; Liu, W. M. Metal-free N, S co-doped graphene for efficient and durable nitrogen reduction reaction. J. Mater. Sci. 2019, 54, 9088–9097.
Crossref Google Scholar
[34]

Pankajakshan, P.; Sanyal, S.; de Noord, O. E.; Bhattacharya, I.; Bhattacharyya, A.; Waghmare, U. Machine learning and statistical analysis for materials science: Stability and transferability of fingerprint descriptors and chemical insights. Chem. Mater. 2017, 29, 4190–4201.
Crossref Google Scholar
[35]

Kumar, N.; Rajagopalan, P.; Pankajakshan, P.; Bhattacharyya, A.; Sanyal, S.; Balachandran, J.; Waghmare, U. V. Machine learning constrained with dimensional analysis and scaling laws: Simple, transferable, and interpretable models of materials from small datasets. Chem. Mater. 2019, 31, 314–321.
Crossref Google Scholar
[36]

Rajan, A. C.; Mishra, A.; Satsangi, S.; Vaish, R.; Mizuseki, H.; Lee, K. R.; Singh, A. K. Machine-learning-assisted accurate band gap predictions of functionalized MXene. Chem. Mater. 2018, 30, 4031–4038.
Crossref Google Scholar
[37]

Xie, T.; Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 2018, 120, 145301.
Crossref Google Scholar
[38]

Ma, J. Y.; Huang, Y. H.; Zhao, H. L.; Ma, F.; Lin, H. P.; Wei, X. M. Efficient screening and catalytic mechanism of TM@β-Te for nitrogen reduction reaction. J. Catal. 2024, 436, 115611.
Crossref Google Scholar
[39]

Hou, P. F.; Huang, Y. H.; Ma, F.; Zhu, G. Q.; Zhang, J. M.; Wei, X. M.; Du, P. Y.; Liu, J. Computational screening and catalytic origin of transition metal supported on g-t-C3N4 as single-atom catalysts for nitrogen reduction reaction. Appl. Surf. Sci. 2022, 599, 153880.
Crossref Google Scholar
[40]

Sun, J. X.; Chen, A. J.; Guan, J. M.; Han, Y.; Liu, Y. J.; Niu, X. H.; He, M. S.; Shi, L.; Wang, J. L.; Zhang, X. Y. Interpretable machine learning-assisted high-throughput screening for understanding NRR electrocatalyst performance modulation between active center and C–N coordination. Energy Environ. Mater. 2024, 7, e12693.
Crossref Google Scholar
[41]

Zhou, J.; Chen, X. Y.; Jiang, X.; Tian, Z. A.; Hu, W. Y.; Huang, B. W.; Yuan, D. W. Machine-learning-accelerated screening of Heusler alloys for nitrogen reduction reaction with graph neural network. Appl. Surf. Sci. 2024, 669, 160519.
Crossref Google Scholar
[42]

Xu, L. T.; Huang, Y. H.; Lin, H. P.; Du, R. H.; Wang, M.; Ma, F.; Wei, X. M.; Zhu, G. Q.; Zhang, J. M. High-throughput screening of carbon nitride single-atom catalysts for nitrogen fixation based on machine learning. J. Mater. Chem. A 2024, 12, 33053–33065.
Crossref Google Scholar
[43]

Gu, Y. M.; Zhu, Q.; Liu, Z. T.; Fu, C.; Wu, J. Y.; Zhu, Q.; Jia, Q. Q.; Ma, J. Nitrogen reduction reaction energy and pathways in metal-zeolites: Deep learning and explainable machine learning with local acidity and hydrogen bonding features. J. Mater. Chem. A 2022, 10, 14976–14988.
Crossref Google Scholar
[44]

Kresse, G.; Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 1996, 54, 11169–11186.
Crossref Google Scholar
[45]

Maintz, S.; Deringer, V. L.; Tchougréeff, A. L.; Dronskowski, R. Analytic projection from plane-wave and PAW wavefunctions and application to chemical-bonding analysis in solids. J. Comput. Chem. 2013, 34, 2557–2567.
Crossref Google Scholar
[46]

Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 1994, 50, 17953–17979.
Crossref Google Scholar
[47]

Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865–3868.
Crossref Google Scholar
[48]

Grimme, S. Semiempirical GGA-type density functional constructed with a long-range dispersion correction. J. Comput. Chem. 2006, 27, 1787–1799.
Crossref Google Scholar
[49]

Mathew, K.; Sundararaman, R.; Letchworth-Weaver, K.; Arias, T. A.; Hennig, R. G. Implicit solvation model for density-functional study of nanocrystal surfaces and reaction pathways. J. Chem. Phys. 2014, 140, 084106.
Crossref Google Scholar
[50]

Mathew, K.; Kolluru, V. S. C.; Mula, S.; Steinmann, S. N.; Hennig, R. G. Implicit self-consistent electrolyte model in plane-wave density-functional theory. J. Chem. Phys. 2019, 151, 234101.
Crossref Google Scholar
[51]

Yu, M.; Trinkle, D. R. Accurate and efficient algorithm for Bader charge integration. J. Chem. Phys. 2011, 134, 064111.
Crossref Google Scholar
[52]

Maintz, S.; Deringer, V. L.; Tchougréeff, A. L.; Dronskowski, R. LOBSTER: A tool to extract chemical bonding from plane-wave based DFT. J. Comput. Chem. 2016, 37, 1030–1035.
Crossref Google Scholar
[53]

Nørskov, J. K.; Rossmeisl, J.; Logadottir, A.; Lindqvist, L.; Kitchin, J. R.; Bligaard, T.; Jónsson, H. Origin of the overpotential for oxygen reduction at a fuel-cell cathode. J. Phys. Chem. B 2004, 108, 17886–17892.
Crossref Google Scholar
[54]

Jiao, D. X.; Liu, Y. J.; Cai, Q. H.; Zhao, J. X. Coordination tunes the activity and selectivity of the nitrogen reduction reaction on single-atom iron catalysts: A computational study. J. Mater. Chem. A 2021, 9, 1240–1251.
Crossref Google Scholar
[55]

Zhang, Y. Q.; Ma, N. G.; Wang, Y. H.; Liang, B. C.; Fan, J. Theoretical design toward highly efficient single-atom catalysts for nitrogen reduction by regulating the “acceptance–donation” mechanism. Appl. Surf. Sci. 2023, 623, 156827.
Crossref Google Scholar
[56]

Yu, L. K.; Li, F. Y.; Huang, J. S.; Sumpter, B. G.; Mustain, W. E.; Chen, Z. F. Double-atom catalysts featuring inverse sandwich structure for CO2 reduction reaction: A synergetic first-principles and machine learning investigation. ACS Catal. 2023, 13, 9616–9628.
Crossref Google Scholar
[57]

Tao, H. B.; Fang, L. W.; Chen, J. Z.; Yang, H. B.; Gao, J. J.; Miao, J. W.; Chen, S. L.; Liu, B. Identification of surface reactivity descriptor for transition metal oxides in oxygen evolution reaction. J. Am. Chem. Soc. 2016, 138, 9978–9985.
Crossref Google Scholar
[58]

Zhang, R. X.; Shehzad, N.; Zhang, L. X.; Ali, A.; Amin, B.; Shahid, I. Efficient electrocatalysts of single metal atom supported on defective graphene for oxygen reduction reaction (ORR): A first principles study. Chem. Phys. 2023, 570, 111888.
Crossref Google Scholar
Nano Research
Volume 18 Issue 4,
April 2025
Article number: 94907289
DOI: 10.26599/NR.2025.94907289
Cite this article:
Xu L, Huang Y, Lin H, et al. Machine learning driven high-throughput screening of S and N-coordinated SACs for eNRR. Nano Research, 2025, 18(4): 94907289. https://doi.org/10.26599/NR.2025.94907289

682

Views

292

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 28 November 2024
Revised: 10 January 2025
Accepted: 06 February 2025
Published: 20 March 2025
© The Author(s) 2025. Published by Tsinghua University Press.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Return