Conspectus The variability of chemical bonding in open-shell transition-metal complexes not
only motivates their study as functional materials and catalysts but also challenges …

Conspectus Computer-aided synthesis planning (CASP) is focused on the goal of
accelerating the process by which chemists decide how to synthesize small molecule …

As physical chemistry transitioned to computational chemistry, a new growth occurred in the
field with the advent of predictive catalysis, making it a key player in the optimization and …

Artificial intelligence (AI) has been referred to as the “fourth paradigm of science,” and as
part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically …

Recently, supervised machine learning has been ascending in providing new predictive
approaches for chemical, biological, and materials sciences applications. In this …

During the last decade, modern machine learning has found its way into synthetic chemistry.
Some long‐standing challenges, such as computer‐aided synthesis planning (CASP), have …

Rational design of compounds with specific properties requires understanding and fast
evaluation of molecular properties throughout chemical compound space—the huge set of …

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to
reveal predictive structure–property relationships. For many properties of interest in …

Established molecular machine learning models process individual molecules as inputs to
predict their biological, chemical, or physical properties. However, such algorithms require …

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