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.

What if scientists could get a taste of discovery as soon as their experiment finishes? Thanks to a new machine learning tool called DONUT, researchers at the U.S. Department of Energy's (DOE) Argonne ...

Professor Toshiaki Taniike is pioneering the integration of data science and materials chemistry to accelerate the discovery of advanced materials. His research in materials informatics uses machine ...

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.

research laboratory to study “bad bubbles” that cause defects in metal alloys used to produce engine turbine blades and semiconductor crystals that are crucial components in electronic devices.

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.

Materials are a necessity for all engineering applications. Materials science and engineering seeks to understand the fundamental physical origins of material behavior in order to optimize properties ...

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 ...

For decades, parents have warned children to stay away from dangerous objects. In 1950, however, one American company did the ...

This chapter is divided into two sections. The first section is an overview of the materials-science microgravity research program implemented by the National Aeronautics and Space Administration ...