Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Flow chemistry has unlocked a world of possibilities for the synthetic community, but the idea
that it is a mysterious “black box” needs to go. In this review, we show that several of the …

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 …

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

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 …

The realization of automated chemical experiments by robots unveiled the prelude to an
artificial intelligence (AI) laboratory. Several AI-based systems or robots with specific …

In recent years, there has been a dramatic rise in interest in retrosynthesis prediction with
artificial intelligence (AI) techniques. Unlike conventional retrosynthesis prediction …

A closed-loop, autonomous molecular discovery platform driven by integrated machine
learning tools was developed to accelerate the design of molecules with desired properties …

Despite advances in machine learning for accurately predicting material properties,
forecasting the performance of thermosetting polymers remains a challenge due to the …

Quantitative chemical reaction data, including activation energies and reaction rates, are
crucial for developing detailed kinetic mechanisms and accurately predicting reaction …