Research in chemistry increasingly requires interdisciplinary work prompted by, among
other things, advances in computing, machine learning, and artificial intelligence. Everyone …

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …

Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …

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 …

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives
(eg, yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein …

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 …

High-throughput experimentation (HTE) is an increasingly important tool in reaction
discovery. While the hardware for running HTE in the chemical laboratory has evolved …

Nuclear magnetic resonance (NMR) is one of the primary techniques used to elucidate the
chemical structure, bonding, stereochemistry, and conformation of organic compounds. The …

Advances in the field of goal-directed molecular optimization offer the promise of finding
feasible candidates for even the most challenging molecular design applications. One …

The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …