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Databases of computational results hold high promise for accelerating catalysis research. Still, many challenges remain and consensus on facets such as metadata, reliability and curation is crucial to transform the hype into an attractive technology.
Catalysis can contribute in many ways to achieving the UN Sustainable Development Goals. Here, the opportunities arising through the interplay of biomass valorization and distributed production approaches are discussed.
This year marks a century since the pioneering work leading to what is now known as the Rosenmund reduction. We celebrate this landmark, reflecting upon the evolution of synthetic methodologies for reductive aldehyde synthesis from carboxylic acid derivatives and highlighting modern, improved strategies.
Industrial research of new catalysts has benefited from both insight and predictions from first-principles calculations. We now find ourselves on the brink of a digital transformation where multiscale approaches and machine-learning methods promise to revolutionize the field.
Catalysis is a complex, multidimensional and multiscale field of research. Machine learning is helping to build better models, understand catalysis research and generate new knowledge about catalysis.
Catalysis is a subject with a surprisingly long and rich history. It seems certain that it has an even brighter future as the challenges of our society require a focus on this discipline more than ever.
