Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

The high-performance self-supported electrocatalysts for the hydrogen evolution reaction are systematically summarized.

Hollow SnO ₂ nanospheres with oxygen vacancies simultaneously wrapped by a nitrogen-doped graphene network (SnO _2−x /N-rGO) improve the electrochemical performance of LIBs.

Lithium–sulfur batteries (LSBs) have gained considerable attention for their desirable energy densities, high theoretical capacities, low cost and environmentally friendly properties. However, the shuttle effect of polysulfides seriously hinders their future practical applications. Herein, a dual-function cathode structure, consisting of 3D porous FeP/rGO microspheres supported on both aluminum foil and a commercial separator, exhibits excellent performance by providing strong adsorption with respect to Li₂S_x (x = 1, 2, 4, 6 and 8) and S₈. In this rational design, the iron phosphide (FeP) nanoparticles act as a catalyst to accelerate polysulfide conversion and as the designated sites for adsorption. The 3D rGO porous conductive network can provide enough space for sulfur loading and to physically adsorb the polysulfides. More importantly, density functional theory (DFT) calculations also verified the strong interactions (with adsorption energy values of −4.21 to −1.97 eV) between the FeP(111) surface and the sulfur species. The electrochemical results show that the cell using the dual-function cathode structure delivers a capacity of 925.7 mA h g⁻¹, with capacity degradation of 0.05% per cycle after 500 cycles, at a current density of 0.5C. It is also worth mentioning that the cell with sulfur loading of ∼2.2 mg cm⁻² maintained a high capacity of 483 mA h g⁻¹ at 0.5C after 500 cycles. In summary, the above results demonstrate the promising application of the dual-function cathode structure for high-performance LSBs.

An ‘ion reservoir’, from an internal electric field and lower Li ⁺ /Na ⁺ adsorption energies at an anatase/TiO ₂ (B) interface, ameliorated Li ⁺ /Na ⁺ storage.