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Page 18 of 25 Meng et al. J. Mater. Inf. 2025, 5, 3 https://dx.doi.org/10.20517/jmi.2024.74

*
Figure 10. (A) Schematic diagram illustrating how TM centers interact with NRR intermediates ( N as an example) on TM/g-CN; (B)
*
Linear relationship between ICOHP and the adsorption energy of N on TM/g-CN. Reprinted with permission from Ref. [42] . Copyright ©
2020, American Chemical Society; (C) and (D) Linear relationships between ICOHP and ΔG N2-NNH on TM@N -G and TM@g-C N.
2
6
Reprinted with permission from Ref. [125] . Copyright © 2022, Elsevier B. V. TM: Transition metal; NRR: nitrogen reduction reaction;
ICOHP: integrated crystal orbital Hamilton population.
Multiple-level descriptors
Beyond adsorption energy and electronic properties, multiple-level descriptors have also been explored to
capture more nuanced aspects of catalytic performance. Among others, Niu et al. proposed a descriptor, φ =
, where N is the number of TM-d orbital electrons, and E is the electronegativity of TM atoms, to
TTM d TM
[128]
predict NRR activity . Nong et al. proposed a descriptor, φ = θ+ α∑(ni + χi)]/χn(i = C, N), which could
predicate the d-band center of TMs anchored on C N, where θ is the number of TM atoms, χ , χ and χ are
N
3
C
M
the electronegativity of TM, C and N atoms, respectively, n (i = C, N) represents the number of the nearest-
i
neighbor C/N, and α is set as 1 . Zheng et al. developed another electronic descriptor, φ = N), where N
n
[128]
i=1
i
is the d orbital electrons for metal atom or valence-electron for the neighboring N, I is the first ionization
1
energy of M or N, and n is the sum of the number of M or N. This descriptor considers the number of
associated electrons and the first ionization energy of metal and nitrogen atoms, allowing effective
estimation of NRR activity in TM trimers supported on N atoms-doped porous graphene (M -NG)
3
systems .
[129]
These multiple-level descriptors, encompassing both electronic and atomic-level properties, offer promising
avenues for application across various catalytic systems. However, a universally applicable descriptor for
efficiently estimating NRR activity remains to be developed, indicating an ongoing need for further
research.