In Silico Design of Covalent Organic Framework-Based Electrocatalysts

Covalent organic frameworks (COFs) are an emerging type of porous crystalline material for efficient catalysis of the oxygen evolution reaction (OER). However, it remains a grand challenge to address the best candidates from thousands of possible COFs. Here, we report a methodology for the design of the best candidate screened from 100 virtual M-N O (M = 3d transition metal)-based model catalysts via density functional theory (DFT) and machine learning (ML). The intrinsic descriptors of OER activity of M-N O were addressed by the machine learning and used for predicting the best structure with OER performances. One of the predicted structures with a Ni-N₂O₂ unit is subsequently employed to synthesize the corresponding Ni-COF. X-ray absorption spectra characterizations, including XANES and EXAFS, validate the successful synthesis of the Ni-N₂O₂ coordination environment. The studies of electrocatalytic activities confirm that Ni-COF is comparable with the best reported COF-based OER catalysts. The current density reaches 10 mA cm^-2 at a low overpotential of 335 mV. Furthermore, Ni-COF is stable for over 65 h during electrochemical testing. This work provides an accelerating strategy for the design of new porous crystalline-material-based electrocatalysts.