Modification of metals and ligands in two-dimensional conjugated metal-organic frameworks for CO electroreduction: A combined density functional theory and machine learning study.

Electrochemical carbon dioxide reduction reaction (CORR) is a promising technology to establish an artificial carbon cycle. Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) with high electrical conductivity have great potential as catalysts. Herein, we designed a range of 2D c-MOFs with different transition metal atoms and organic ligands, TMNO-HDQ (TM = Cr∼Cu, Mo, Ru∼Ag, W∼Au; x  = 0, 2, 4; HDQ = hexadipyrazinoquinoxaline), and systematically studied their catalytic performance using density functional theory (DFT). Calculation results indicated that all of TMNO-HDQ structures possess good thermodynamic and electrochemical stability. Notably, among the examined 37 MOFs, 6 catalysts outperformed the Cu(211) surface in terms of catalytic activity and product selectivity. Specifically, NiN-HDQ emerged as an exceptional electrocatalyst for CO production in CORR, yielding a remarkable low limiting potential (U) of -0.04 V. CuN-HDQ, NiNO-HDQ, and PtNO-HDQ also exhibited high activity for HCOOH production, with U values of -0.27, -0.29, and -0.27 V, respectively, while MnN-HDQ, and NiO-HDQ mainly produced CH with U values of -0.58 and -0.24 V, respectively. Furthermore, these 6 catalysts efficiently suppressed the competitive hydrogen evolution reaction. Machine learning (ML) analysis revealed that the key intrinsic factors influencing CORR performance of these 2D c-MOFs include electron affinity (E), electronegativity (χ), the first ionization energy (I), p-band center of the coordinated N/O atom (ε), the radius of metal atom (r), and d-band center (ε). Our findings may provide valuable insights for the exploration of highly active and selective CORR electrocatalysts.