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• 19.07.2019

A special issue on “Computational Design of Catalysts” appeared in the renowned journal Chemical Reviews

Chemical Reviews, a renowned journal in the field of chemistry, published a special issue, with the focus on computational chemistry in the field of the design of catalysts (Chem. Rev. 2019, 119 (11), 6507 – 6768). This emphasises the importance computational chemistry has reached nowadays in the design of catalysts and the elucidation of reaction mechanisms.

The individual review articles describe approaches and novelties in the fields of catalyst design (here especially ligand systems for organic and organometallic reactions), in silico catalyst design through artificial intelligence by an inverse design approach, the design of enzyme catalysts, heterogeneous (photo)electrochemical reduction of CO2, and the prediction of post-synthesis behaviour of 2D and 3D nanocrystals.

“The increased availability of computational resources and the development of more efficient and more accurate computational methods allowed for an active and predictive role of computational chemistry in the process of catalyst development. Thereby, it reduces cost and time and gives inspiration for novel experimental efforts.”[1]

“Based on the complexity of catalytic systems, no single computational strategy has emerged and needs to be tailored for the catalytic system in question. Computational results always contain a trade-off between information and accuracy provided on either the reaction mechanism or the physical property of the matieral.”[1]

“The interplay between experimental methods and computational chemistry shows its potential for catalyst screening and provides experimentally testable hypothesis.”[1]

This special issue in Chemical Reviews emphasises the versatile opportunities and the great potential of computational chemistry in the design of homogeneous, heterogeneous, and biocatalysts, as well as the prediction of reaction pathways. These methods are highly efficient in high-throughput screening of catalysts. Artificial intelligence supports the design and predictions in many ways. Well known large chemical and pharmaceutical companies already recognized this potential of computational chemistry and developed therefore own capabilities in this field.