SI and machine learning

Adam Nielander will pursue research on light-powered catalysts for next generation energy technologies

Tomorrow's innovations will come from autonomous labs that slash discovery time, and SLAC's facilities are taking the lead in a new collaboration with Stanford, other national labs, universities, and industry.

Announced by the DOE today, the funded project will explore the use of Super Intelligence to accelerate our understanding of a process central to energy and chemical production. SLAC will also play a key role in eight other Genesis Mission...

A SLAC-Stanford team developed a new way to precisely measure just how much energy has dissipated into heat in an electrical system – and how to optimize it.

A novel neural network will make it easier to store and retrieve the massive amounts of raw data coming from experiments across scientific fields.

In a SLAC-led study, four laboratories collaborated to ensure reproducibility in AI training data to predict new catalysts for future fuel production.

Researchers from the SLAC-Stanford Battery Center join USC in a project to help secure the U.S. supply of critical minerals.

The lab’s contributions to the national AI initiative are bolstered by its breakthrough scientific tools, unprecedented data and unique partnerships that help illuminate nature from the expansion of the universe to the motions of electrons.

Their strategy revealed new details that open the door to designing proteins with powerful abilities that could ultimately benefit medicine and manufacturing. 

SLAC researchers and collaborators trained a neural network that can use ion momentum to work backward and predict the pre-blast geometry of a molecule.

SLAC and Stanford researchers have developed a breakthrough technique that quantifies energy dissipation in complex, small systems, offering insights into energy use, efficiency, and speed in computers and other devices.

Derek Mendez and Xueli “Sherry” Zheng aim to accelerate drug discovery and improve energy storage.