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 …

From the start of a synthetic chemist's training, experiments are conducted based on recipes
from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist …

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 …

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

The year 2021 marks the 125th anniversary of the Bayer Chemical Research Laboratory in
Wuppertal, Germany. A significant number of prominent small-molecule drugs, from Aspirin …

Machine learning (ML) integrated density functional theory (DFT) calculations have recently
been used to accelerate the design and discovery of heterogeneous catalysts such as single …

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

Methods to functionalize arenes and heteroarenes in a site-selective manner are highly
sought after for rapidly constructing value-added molecules of medicinal, agrochemical, and …

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

The physical sciences community is increasingly taking advantage of the possibilities
offered by modern data science to solve problems in experimental chemistry and potentially …