Argonne and Pacific Northwest national laboratories are applying a suite of machine-learning techniques to better understand why water behaves so much differently than other liquids.
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.
A new machine learning technique that uses data from high-energy X-ray diffraction experiments will significantly reduce model development time and human effort.