4.6 Article

Understanding activity origin for the oxygen reduction reaction on bi-atom catalysts by DFT studies and machine-learning

Journal

JOURNAL OF MATERIALS CHEMISTRY A
Volume 8, Issue 46, Pages 24563-24571

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/d0ta08004g

Keywords

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Funding

  1. Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN201801608]
  2. Program of Innovation Center for Lipid Resource Utilization at Chongqing University of Education [2017XJPT01]
  3. Program for Innovative Research Team in Chongqing University of Education [CQYC201903178]
  4. National Natural Science Foundation of China [21903008]
  5. Chongqing Municipal Resources and Society Security Bureau [cx2019141]
  6. Chongqing Science and Technology Commission [cstc2020jcyjmsxmX0382]
  7. Fundamental Research Funds for the Central Universities [2020CDJQY-A031, 2020CDJ-LHZZ-063]
  8. NSF Center for the Advancement of Wearable Technologies [1849243]

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Bi-atom catalysts (BACs) have attracted increasing attention in important electrocatalytic reactions such as the oxygen reduction reaction (ORR). Here, by means of density functional theory simulations coupled with machine-learning technology, we explored the structure-property correlation and catalytic activity origin of BACs, where metal dimers are coordinated by N-doped graphene (NC). We first sampled 26 homonuclear (M-2/NC) BACs and constructed the activity volcano curve. Disappointingly, only one BAC, namely Co-2/NC, exhibits promising ORR activity, leaving considerable room for enhancement in ORR performance. Then, we extended our study to 55 heteronuclear BACs (M1M2/NC) and found that 8 BACs possess competitive or superior ORR activity compared with the Pt(111) benchmark catalyst. Specifically, CoNi/NC shows the most optimal activity with a very high limiting potential of 0.88 V. The linear scaling relationships among the adsorption free energy of *OOH, *O and *OH species are significantly weakened on BACs as compared to a transition metal surface, indicating that it is difficult to precisely describe the catalytic activity with only one descriptor. Thus, we adopted machine-learning techniques to identify the activity origin for the ORR on BACs, which is mainly governed by simple geometric parameters. Our work not only identifies promising BACs yet unexplored in the experiment, but also provides useful guidelines for the development of novel and highly efficient ORR catalysts.

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