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

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 …

Summary Self-driving laboratories (SDLs), which combine automated experimental
hardware with computational experiment planning, have emerged as powerful tools for …

The development of automated high-throughput experimental platforms has enabled fast
sampling of high-dimensional decision spaces. To reach target properties efficiently, these …

Research process automation–the reliable, efficient, and reproducible execution of linked
sets of actions on scientific instruments, computers, data stores, and other resources–has …

This review proposes the concept of a “frugal twin,” similar to a digital twin, but for physical
experiments. Frugal twins range from simple toy examples to low-cost surrogates of high …

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and
advanced computing to accelerate scientific discovery. The promise of this field has given …

While abstraction is critical for the transferability of automated laboratory science in (bio)
chemical and materials sciences, its improper implementation is a technical debt taken …

Advancements in artificial intelligence (AI) for science are continually expanding the value
proposition for automation in materials and chemistry experiments. The advent of …

Algorithmic materials discovery is a multidisciplinary domain that integrates insights from
specialists in alloy design, synthesis, characterization, experimental methodologies …