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 …

Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Over the last decades, excellent computational chemistry tools have been developed.
Integrating them into a single platform with enhanced accessibility could help reaching their …

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

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 …

Depolymerization is a promising strategy for recycling waste plastic into constituent
monomers for subsequent repolymerization. However, many commodity plastics cannot be …

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 …

Large language models (LLMs) have shown strong performance in tasks across domains
but struggle with chemistry-related problems. These models also lack access to external …

The development of late-stage functionalization (LSF) methodologies, particularly C–H
functionalization, has revolutionized the field of organic synthesis. Over the past decade …

Over the past decade, the landscape of molecular synthesis has gained major impetus by
the introduction of late-stage functionalization (LSF) methodologies. C–H functionalization …