A computational modeling suite for toxicity estimation of PFAS ​“forever chemicals”

Argonne is developing a physics-informed machine learning approach to discover compatible cathodes, thereby overcoming barriers to commercialization of solid-state lithium-ion batteries.

Implementation of a machine-learning-based technique provides a new level of sensitivity for data collected in nuclear reactions.

An Argonne team has developed a machine learning approach for calibrating the center of rotation in x-ray light source tomography data that provides better accuracy than conventional imaging processing-based methods.

From studying a sea slug, researchers have demonstrated a fundamental type of learning in an inorganic system that may serve as a building block for neuromorphic computing and AI applications.

Deep learning enables real-time 3D nanoscale imaging with X-rays.

A new machine learning technique that uses data from high-energy X-ray diffraction experiments will significantly reduce model development time and human effort.

A new machine learning approach significantly lowers the barrier to understanding and designing molten salts across the periodic table.

Argonne is applying machine learning techniques to obtain probabilistic forecasts of wind.

Argonne is using artificial intelligence to identify particles traversing 10-story tall particle detector at the Large Hadron Collider in Geneva Switzerland.