Large amounts of data are generated in chemistry labs—nearly all instruments record data
in a digital form, yet a considerable proportion is also captured non-digitally and reported 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 development of machine learning models for electrocatalysts requires a broad set of
training data to enable their use across a wide variety of materials. One class of materials …

Thermocatalytic CO 2 hydrogenation to methanol is an attractive defossilization technology
to combat climate change while producing a valuable platform chemical and energy carrier …

Modern lean-operated internal combustion engines running on natural gas, biogas, or
methane produced from wind or solar energy are highly fuel-efficient and can greatly …

Light‐driven homogeneous and heterogeneous catalysis require a complex interplay
between light absorption, charge separation, charge transfer, and catalytic turnover. Optical …

The recent rise of computational, data-driven research has significant potential to accelerate
materials discovery. Automated workflows and materials databases are being rapidly …

Data science and machine learning have the potential to accelerate the discovery of
effective catalysts; however, these approaches are currently held back by the issue of …

Tackling climate change is one of the undoubtedly most important challenges at the present
time. This review deals mainly with the chemical aspects of the current status for converting …

Designing catalysts is a challenging matter as catalysts are involved with various factors that
impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties …