Electrocatalytic CO2 reduction reactions (CO2RR) based on scalable and highly efficient catalysis provide
an attractive
strategy for reducing CO2 emissions. In this work, we combined
first-principles density functional theory (DFT) and machine learning
(ML) to comprehensively explore the potential of double-atom catalysts
(DACs) featuring an inverse sandwich structure anchored on defective
graphene (gra) to catalyze CO2RR to generate C1 products. We started with five homonuclear M2⊥gra
(M = Co, Ni, Rh, Ir, and Pt), followed by 127 heteronuclear MM′⊥gra
(M = Co, Ni, Rh, Ir, and Pt, M′ = Sc–Au). Stable DACs
were screened by evaluating their binding energy, formation energy,
and dissolution potential of metal atoms, as well as conducting first-principles
molecular dynamics simulations with and without solvent water molecules.
Based on DFT calculations, Rh2⊥gra DAC was found
to outperform the other four homonuclear DACs and the Rh-based single-
and double-atom catalysts of noninverse sandwich structures. Out of
the 127 heteronuclear DACs, 14 were found to be stable and have good
catalytic performance. An ML approach was adopted to correlate key
factors with the activity and stability of the DACs, including the
sum of radii of metal and ligand atoms (d
M–M′, d
M–C, and d
M′–C), the sum and difference of electronegativity
of two metal atoms (P
M + P
M′, P
M – P
M′), the sum and difference of first
ionization energy of two metal atoms (I
M + I
M′, I
M – I
M′), the sum
and difference of electron affinity of two metal atoms (A
M + A
M′, A
M – A
M′), and
the number of d-electrons of the two metal atoms (N
d). The obtained ML models were further used to predict
154 potential electrocatalysts out of 784 possible DACs featuring
the same inverse sandwich configuration. Overall, this work not only
identified promising CO2RR DACs featuring the reported
inverse sandwich structure but also provided insights into key atomic
characteristics associated with high CO2RR activity.