The Sabatier principle, which states that the binding energy between the catalyst and the
reactant should be neither too strong nor too weak, has been widely used as the key …

Machine learning (ML) integrated density functional theory (DFT) calculations have recently
been used to accelerate the design and discovery of heterogeneous catalysts such as single …

Alloying noble metals with non‐noble metals is a promising method to fabricate catalysts,
with the advantages of reduced noble metal usage and excellent activity. In this work …

Introducing defects into 2D materials can increase the coordinatively unsaturated sites that
are usually also catalytically active sites. How does the level of defects influence catalytic …

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry
for the creation and optimization of electrocatalysts, which enhance key electrochemical …

Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative
structure–activity relationships to accelerate electrocatalyst design by learning from historic …

The design and synthesis of a highly active noble metal-free electrocatalyst for hydrogen
evolution reaction (HER) from water splitting are crucial for renewable energy technologies …

Interfacial microenvironment modulation has been proven to be a promising route to
fabricate highly efficient catalysts. In this work, the lattice defect‐rich NiS2/MoS2 nanoflakes …

Electrocatalytic seawater splitting offers a promising avenue for cost-effective and
environmentally friendly hydrogen production. However, the activity of catalysts has …

Conspectus Novel technologies and new materials are in high demand for future various
applications to overcome the fundamental limitations of current techniques. For example, the …