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

This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

Transformer-based large language models are making significant strides in various fields,
such as natural language processing,,,–, biology,, chemistry,–and computer programming …

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

Protein engineering has nearly limitless applications across chemistry, energy and
medicine, but creating new proteins with improved or novel functions remains slow, labor …

Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …

Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires
automated measurements coupled with reliable decision-making,. Most autonomous …

The consideration of discrete variables (eg catalyst, ligand, solvent) in experimental self-
optimization approaches remains a significant challenge. Herein we report the application of …

Abstract Machine learning (ML) methods enable computers to address problems by learning
from existing data. Such applications are becoming commonplace in molecular sciences …

Conspectus The digitization of chemistry is not simply about using machine learning or
artificial intelligence systems to process chemical data, or about the development of ever …