Single-atom alloys (SAAs) play an increasingly significant role in the field of single-site
catalysis and are typically composed of catalytically active elements atomically dispersed in …

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 …

The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

It has been over twenty years since the linear scaling of reaction intermediate adsorption
energies started to coin the fields of heterogeneous and electrocatalysis as a blessing and a …

At present, alloys have broad application prospects in heterogeneous catalysis, due to their
various catalytic active sites produced by their vast element combinations and complex …

Application of machine learning (ML) to the prediction of reaction activation barriers is a new
and exciting field for these algorithms. The works covered here are specifically those in …

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 …

Volcano plots and scaling relations are commonly used to design catalysts and understand
catalytic behavior. These plots are a useful tool due to their robust and simple analysis of …

X-ray absorption spectroscopy (XAS) produces a wealth of information about the local
structure of materials, but interpretation of spectra often relies on easily accessible trends …

Developing physically transparent and quantitatively accurate models that relate the
chemical interaction (chemisorption strength) between an adsorbate and a solid surface to …