Against the backdrop of global warming, population growth, rapid urbanization, and industrial growth in emerging economies have led to a massive increase in worldwide energy consumption and have raised the specter of the rapid depletion of fossil-fuel-based energy resources. In this context of increasing CO2 emissions and diminishing petroleum reserves, the development of clean, environment-friendly, highly efficient, and sustainable energy sources has assumed tremendous importance.

Advances in computational methods, in particular density functional theory (DFT) as well as in high-performance supercomputing mean it is now possible to describe catalytic reactions with the detail and accuracy required for realistic comparison with experiments. In this regard, computational design from first principles can aid the development of robust catalysts by providing valuable insights into the thermodynamic and kinetic details of the mechanistic pathways, catalytic activity, and product selectivity as well as determining the geometric and electronic factors (“descriptors”) governing the various chemical transformations relevant to sustainable energy solutions.

The overall aim of this special issue is to attract high-quality work in the field of computational design of materials/catalysts for various chemical transformations relevant to energy-sustainable solutions. Although DFT is the widely accepted tool for computational catalysts design, recent attention shifted toward the data-drive AI discovery of materials. Priorities will be given to work in the field of ML applications in catalysis. Some of the interesting topics include:

(1) CO2 reduction to fuels such as CO, HCOOH, Methane

(2) N2 reduction to NH3

(3) CO2 photo/electroreduction

(4) H2 storage materials and water splitting

(5) Oxygen evolution reactions

(6) Alkane metathesis

The overall aim of this special issue is to attract high-quality work in the field of computational design of materials/catalysts for various chemical transformations relevant to energy-sustainable solutions. Although DFT is the widely accepted tool for computational catalysts design, recent attention shifted toward the data-drive AI discovery of materials. Priorities will be given to work in the field of ML applications in catalysis. Some of the interesting topics include:

(1) Homogeneous catalysts (i.e. molecular complexes)

(2) Heterogeneous catalysis

(3) Nanocatalysts

(4) Carbocatalysts (i.e. carbon based materials)

(5) 2D material based catalysts.

(6) Organocatalysis