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 combines advances in AI with extreme and edge computing to unlock new opportunities in astrophysics.

Argonne’s 3D X-ray microscope employs computer vision to detect features of interest and capture and visualize data.

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

Argonne is leading an effort aimed at developing artificial neural representations of nuclear quantum-mechanical wave functions.

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

Argonne is working to understand the effectiveness of new AI accelerator systems for scientific machine learning applications.

Argonne is working on more efficient ways to understand the fundamental constituents of matter and the interactions among them.

Argonne is working to enhance the reliability of AI techniques applied to experimental data for the NEXT experiment.

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