NASA and IBM have released a free, open-source artificial intelligence model built to help scientists analyze the Moon's surface, including estimating where water ice could remain stable near the lunar poles. The NASA-IBM Lunar Foundation Model, announced Sept. 10, is available on Hugging Face, with its full code on GitHub, and NASA describes it as among the first open-source AI models built specifically for lunar science. IBM said lunar ice indicates the presence of water and oxygen, resources it described as essential for a future Moon base and for producing rocket fuel, and that knowing where ice is likely to persist can help planners prioritize landing sites and instruments before crews arrive. The release adds to a family of NASA and IBM science models that already cover Earth observation and space weather. According to NASA, the model was trained primarily on data from the Lunar Reconnaissance Orbiter, which has mapped most of the Moon in detail over 17 years and whose data volume is larger than that of all other NASA planetary missions combined. Training used roughly 2 million image tiles, including more than 1 million camera images at 1-meter resolution and nearly 964,000 multispectral images at 100-meter resolution. The team also drew on imagery and terrain data from NASA's GRAIL and Lunar Prospector missions and from JAXA's SELENE, also called Kaguya. IBM said the researchers built a unified, machine learning-ready dataset of more than 30 spatially aligned layers from nine instruments across four missions,