  Development of cost-effective hydrogen evolution reaction (HER) catalysts
with outstanding catalytic activity, replacing cost-prohibitive noble
metal-based catalysts, is critical for practical green hydrogen production. A
popular strategy for promoting the catalytic performance of noble metal-free
catalysts is to incorporate earth-abundant transition metal (TM) atoms into
nanocarbon platforms such as carbon quantum dots (CQDs). Although data-driven
catalyst design methods can significantly accelerate the rational design of TM
element-doped CQD (M@CQD) catalysts, they suffer from either a simplified
theoretical model or the prohibitive cost and complexity of experimental data
generation. In this study, we propose an effective and facile HER catalyst
design strategy based on machine learning (ML) and ML model verification using
electrochemical methods accompanied with density functional theory (DFT)
simulations. Based on a Bayesian genetic algorithm (BGA) ML model, the Ni@CQD
catalyst on a three-dimensional reduced graphene oxide (3D rGO) conductor is
proposed as the best HER catalyst under the optimal conditions of catalyst
loading, electrode type, and temperature and pH of electrolyte. We validate the
ML results with electrochemical experiments, where the Ni@CQD catalyst
exhibited superior HER activity, requiring an overpotential of 189 mV to
achieve 10 mA cm-2 with a Tafel slope of 52 mV dec-1 and impressive durability
in acidic media. We expect that this methodology and the excellent performance
of the Ni@CQD catalyst provide an effective route for the rational design of
highly active electrocatalysts for commercial applications.
