Discover Rare Earth Elements – Scientists use artificial intelligence to find rare materials

By harnessing patterns in mineral associations, a new machine learning model can predict the locations of minerals on Earth and possibly other planets. This advance is of enormous value to science and industry, as they are constantly exploring mineral deposits to unearth the history of the planet and mine for resources for practical applications, such as rechargeable batteries.

A team led by Shona Morrison and Anirudh Prabhu aims to develop a method for determining the occurrence of specific minerals, a goal that has traditionally been seen as an art as much as a science. This process often relied on individual experience combined with a healthy dose of luck.

The team has created a file[{” attribute=””>machine learning model that uses data from the Mineral Evolution Database, which includes 295,583 mineral localities of 5,478 mineral species, to predict previously unknown mineral occurrences based on association rules.

The authors tested their model by exploring the Tecopa basin in the Mojave Desert, a well-known Mars analog environment. The model was also able to predict the locations of geologically important minerals, including uraninite alteration, rutherfordine, andersonite, and schröckingerite, bayleyite, and zippeite.

In addition, the model located promising areas for critical rare earth elements and lithium minerals, including monazite-(Ce), and allanite-(Ce), and spodumene. Mineral association analysis can be a powerful predictive tool for mineralogists, petrologists, economic geologists, and planetary scientists, according to the authors.

Reference: “Predicting new mineral occurrences and planetary analog environments via mineral association analysis” by Shaunna M Morrison, Anirudh Prabhu, Ahmed Eleish, Robert M Hazen, Joshua J Golden, Robert T Downs, Samuel Perry, Peter C Burns, Jolyon Ralph and Peter Fox, 16 May 2023, PNAS Nexus.
DOI: 10.1093/pnasnexus/pgad110