The field of predictive chemistry relates to the development of models able to describe how
molecules interact and react. It encompasses the long-standing task of computer-aided …

Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …

The development of highly efficient and economical materials for the oxygen reduction
reaction (ORR) plays a key role in practical energy conversion technologies. However, the …

Organic chemistry is replete with complex relationships: for example, how a reactant's
structure relates to the resulting product formed; how reaction conditions relate to yield; how …

The dependence on fossil fuels has caused excessive emissions of greenhouse gases
(GHGs), leading to climate changes and global warming. Even though the expansion of …

The quest for enhanced electrocatalysts can be boosted by descriptor‐based analyses.
Because adsorption energies are the most common descriptors, electrocatalyst design is …

Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative
structure–activity relationships to accelerate electrocatalyst design by learning from historic …

In recent years, the role of bioinformatics and computational biology together with omics
techniques and transcriptomics has gained tremendous importance in biomedicine and …

Synthetic chemistry has traditionally relied on reactions between reactants of high chemical
potential and transformations that proceed energetically downhill to either a global or local …

Autonomous reaction network exploration algorithms offer a systematic approach to explore
mechanisms of complex chemical processes. However, the resulting reaction networks are …