Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables.

This chapter contains descriptions of the broad categories of microgravity materials-science experiments that could yield significant information that is unattainable in a terrestrial gravity field.

When you need tools or parts for something you’re working on around the house, you head to the nearest hardware store. Space travelers don’t have that luxury and may have to make their own tools and ...

Self-driving laboratories (SDLs) — research systems that combine robotics, AI, and autonomous experimentation — are transforming discoveries of new and better materials. Capable of running and ...

Materials science explores the structure and properties of materials — semiconductors, metals, ceramics, polymers, composites and more — to understand and improve their performance. From smartphone ...

Scientists have developed an AI-based method that helps gather data more efficiently in the search for new materials, allowing researchers to navigate complex design challenges with greater precision ...

We are entering a new era in science — the fourth paradigm, according to Kristin Persson, a professor in materials science at the University of California in Berkeley, United States. The first ...

This collection of lessons, videos and resources is designed for teachers, students and their families, exploring the science of materials from everyday applications to groundbreaking innovations.

Researchers have found an efficient way to identify 'topological' materials, whose surfaces can have different electrical or functional properties than their interiors. The approach should make it ...

The microgravity environment (10-6 g) of space provides a unique opportunity to further our understanding of various materials phenomena involving the molten, fluidic, and gaseous states by reducing ...