Assessing the outcomes of chemical reactions in a quantitative fashion has been a
cornerstone across all synthetic disciplines. Classically approached through empirical …

Models can codify our understanding of chemical reactivity and serve a useful purpose in
the development of new synthetic processes via, for example, evaluating hypothetical …

The chemical sciences are witnessing an influx of statistics into the catalysis literature.
These developments are propelled by modern technological advancements that are leading …

As more data are introduced in the building of models of chemical reactivity, the mechanistic
component can be reduced until 'big data'applications are reached. These methods no …

Conspectus Numerous disciplines, such as image recognition and language translation,
have been revolutionized by using machine learning (ML) to leverage big data. In organic …

Data-driven chemistry has been described as the “future” of industrial organic synthesis that
“will increasingly help guide synthetic chemists through the toughest synthesis problems”, in …

In the fine chemicals industry, reaction screening and optimisation are essential to
development of new products. However, this screening can be extremely time and labor …

Conspectus The incorporation of data science is revolutionizing organic chemistry. It is
becoming increasingly possible to predict reaction outcomes with accuracy, computationally …

Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

Recent literature suggests that the fields of machine learning (ML) and high-throughput
experimentation (HTE) have separately received considerable attention from chemists and …