MLExchange is a software framework designed to facilitate collaboration among researchers, provide ML solutions, and reduce the barriers to use ML tools.
New data framework will enhance understanding of AI, provide new insights to apply AI techniques, and provide an environment to explore novel approaches to AI.
Argonne is developing a physics-informed machine learning approach to discover compatible cathodes, thereby overcoming barriers to commercialization of solid-state lithium-ion batteries.
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.