The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

In the last five years, there has been tremendous growth in machine learning and artificial
intelligence as applied to polymer science. Here, we highlight the unique challenges …

Nanotechnology is the study and application of materials, structures, devices, and systems
that are based on phenomena at the nanoscale, which is about one hundred nanometers or …

Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …

Abstract Machine learning is rapidly becoming an integral part of experimental physical
discovery via automated and high‐throughput synthesis, and active experiments in …

Scientific advancement is universally based on the dynamic interplay between theoretical
insights, modeling, and experimental discoveries. However, this feedback loop is often slow …

Abstract Machine learning (ML) has become a valuable tool to assist and improve materials
characterization, enabling automated interpretation of experimental results with techniques …

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in
material characterization and synthesis requires strategies development for understanding …

Autonomous materials research systems allow scientists to fail smarter, learn faster, and
spend less resources in their studies. As these systems grow in number, capability, and …

Numerous critical technologies are currently materials-limited, awaiting novel materials
solutions for advancement. Examples include transportation (light-weight, high-strength …