Artificial Intelligence

Prof Bilge Yildiz and colleagues devised a machine-learning-based method to investigate how materials behave at their surfaces. The approach could help in developing compounds or alloys for use as catalysts, semiconductors, or battery components.

The inaugural MIT-Pillar AI Collective Fellows are (from left to right) Alexander Andonian, Daniel Magley, and Madhumitha Ravichandra.

Jossou is one of eleven new faculty members join six of the School of Engineering's academic departments and institutes.

Knowing the magnetic structure of crystalline materials is critical to many applications, including data storage, high-resolution imaging, spintronics, superconductivity, and quantum computing. Information of this sort, however, is difficult to come by. Although magnetic structures can be obtained from neutron diffraction and...

It is a problem that has beguiled scientists for a century. But, buoyed by a $625,000 Distinguished Early Career Award from the U.S. Department of Energy (DoE), Matteo Bucci, an associate professor in the Department of Nuclear Science and Engineering (NSE), hopes...

Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.

Workshop hosted by MIT’s Climate and Sustainability Consortium, MIT-IBM Watson AI Lab, and the MIT Schwarzman College of Computing highlights how new approaches to computing can save energy and help the planet.

The winners of the first-ever Climate Grand Challenges will become multiyear flagship research projects, helping define a new research agenda focused on addressing complex unsolved climate problems and bringing high-impact solutions to the world on an accelerated basis.

MIT researchers discovered hidden magnetic properties in multi-layered electronic material by analysing polarized neutrons using neural networks. Superconductors have long been considered the principal approach for realizing electronics without resistivity. In the past decade, a new family of quantum materials, “topological materials,”...