NEWSROOM

Michele Simoncelli’s group introduces a new benchmark to evaluate how well machine learning models for atomic interactions translate quantum characteristics into macroscopic physical properties

Artificial intelligence is transforming the way we work and live, but training AI models requires enormous amounts of computing power and energy. Finding new ways to make AI faster and more energy efficient is becoming one of the biggest challenges in the field. Researchers from the R. Ferreira group at INL have developed a new type of spintronic hardware that allows artificial intelligence to potentially learn directly on the chip, making the learning process more efficient.

The intricate dance of atoms fusing and releasing energy has fascinated scientists for decades. Now, human ingenuity and artificial...

Materials science enables cutting-edge technologies, from lightweight cars and powerful computers to high-capacity batteries and durable...

A research team, comprising Professor Junsuk Rho from the Department of Mechanical Engineering, the Department of Chemical Engineering,...

Atomic force microscopy, or AFM, is a widely used technique that can quantitatively map material surfaces in three dimensions, but its...