SHINE Selected for DOE AI Nuclear Fuel Recycling Projects

Aug 4, 2026
SHINE was selected for two U.S. Department of Energy Genesis Mission projects, including one it will lead to apply AI to nuclear fuel recycling process design.

SHINE has been selected for two U.S. Department of Energy Genesis Mission projects, including one it will lead on AI for nuclear fuel recycling, the company said in a press release.

SHINE will be the prime recipient on a Phase I project focused on AI guided fuel cycle facility optimization. Argonne National Laboratory is the technical partner. The project aims to create an AI workflow that helps engineers compare recycling process designs across performance, product quality, and waste.

The work will build on Argonne tools called AMUSE, which models separation chemistry for used fuel, and ARTEMIS, which models how a recycling facility fits together. SHINE said the added AI layer will help engineers examine more design options for recycling used nuclear fuel.

SHINE will also join Prometheus, a Phase II Genesis Mission project led by Idaho National Laboratory. The consortium will apply AI across nuclear energy, with SHINE contributing data from its recycling work and gaining early access to tools developed through the project.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like AI Policy Brief, Industrial AI Weekly or Daily AI Brief.

Also, consider following us on social media:

AI Safety & Regulation AI Brief AI Brief (X)
Free newsletter

Industrial AI Weekly

The latest advancements in smart manufacturing, predictive maintenance, AI-driven quality control, supply chain & more.

Guide
AI meeting notetakers for workplace conversations

AI meeting notetakers for workplace conversations

A guide to capture methods, meeting notes and team workflows

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.