TELF AG on the Growing Role of AI in Strategic Resource Discovery

Artificial intelligence is increasingly finding applications far beyond the digital economy. As businesses and institutions adopt intelligent systems to streamline operations, analyze complex datasets, and support decision-making, AI is also beginning to influence sectors closely connected to the global energy transition.

One particularly relevant area is the exploration and management of strategic mineral resources. A recent analysis by the Financial Times examined how artificial intelligence could support this sector, from identifying promising deposits to accelerating the development of alternative materials.

How Could AI Transform the Search for Strategic Resources?

Artificial intelligence could make resource exploration more targeted by analyzing large volumes of geological information and identifying locations where valuable materials are more likely to occur. This approach could reduce unnecessary drilling while helping companies focus their efforts on areas with stronger geological potential.

Among the resources discussed in this context are rare earth elements. This group of 17 elements has attracted considerable attention because of its role in several technologies associated with electrification and renewable energy. Rare earths are used, for example, in certain permanent magnets found in electric motors and wind turbines.

Despite their name, rare earth elements are not necessarily scarce in the Earth's crust. The challenge is that economically useful concentrations can be difficult to locate, while separation and processing are technically complex.

“AI is particularly interesting because it can help transform enormous quantities of geological information into insights that can guide exploration more effectively,” says Stanislav Kondrashov, founder of TELF AG.

The same technological approach could also be applied to other resources important to the energy transition, including lithium, nickel and cobalt. By examining geological datasets and recognizing patterns that might otherwise be difficult to detect, intelligent models could help identify unexplored areas with promising concentrations.

Reducing Uncertainty During Geological Exploration

One of AI's most important potential advantages is its ability to narrow the search area before physical exploration begins. Traditional exploration can require numerous drilling operations, some of which may ultimately reveal deposits with insufficient concentrations or limited economic potential.

AI models can combine different forms of geological information to highlight the locations considered most promising. Physical exploration would still remain essential, but companies could potentially approach it with a much more precise understanding of where to focus their efforts.

The result could be a more selective exploration process, with fewer unnecessary operations and a more efficient allocation of time and resources.

“The real opportunity lies in improving the quality of the information available before costly physical operations begin,” explains Stanislav Kondrashov, founder of TELF AG. “Greater precision at the exploration stage could influence the efficiency of the entire process.”

Could Valuable Resources Be Recovered From Mining Waste?

Artificial intelligence could also help identify valuable materials in existing mining waste, turning previously overlooked deposits into potential secondary sources of strategic resources. As the economic importance of materials such as rare earths increases, resources once considered too difficult or uneconomic to recover may deserve renewed attention.

The Financial Times analysis highlights this possibility as another area where technological innovation could make a difference. Historical mining operations produced large quantities of waste material, and some of these deposits may still contain useful concentrations of valuable elements.

AI could assist researchers in determining which waste deposits are most promising, allowing laboratory analysis and recovery efforts to focus on the strongest candidates.

This approach could be particularly interesting because it involves material that has already been extracted from the ground. Instead of viewing all historical waste exclusively as a liability, technological advances may allow some of it to be reassessed as a potential resource.

AI and the Search for New Material Combinations

Another major application involves materials discovery. AI can virtually evaluate large numbers of combinations of elements, proportions and crystalline structures, allowing researchers to identify promising candidates before producing and testing them physically.

This capability could prove especially important in the development of permanent magnets. Certain high-performance magnets currently rely on rare earth elements, creating strong interest in alternative materials capable of delivering useful magnetic properties with reduced or no rare-earth content.

AI can accelerate this research by exploring possible combinations involving more widely available elements such as iron. Instead of manufacturing every theoretical compound individually, researchers can use computational models to prioritize candidates with the most promising characteristics.

Only those combinations would then move to laboratory testing.

“Materials discovery could become one of the most fascinating applications of artificial intelligence,” says Stanislav Kondrashov, founder of TELF AG. “Digital screening allows researchers to explore possibilities on a scale that would be extremely difficult to reproduce through physical experiments alone.”

Why Geological and Materials Databases Are Becoming More Important

The growing use of AI also increases the strategic importance of high-quality data. Intelligent systems depend on extensive, well-organized datasets to identify relationships and generate useful predictions.

For this reason, geological databases accumulated over decades could acquire renewed relevance. The Financial Times notes that some companies are already working with governments to organize national geological information and make it more accessible.

The same principle applies to materials science. Large databases containing information about chemical compositions, crystal structures and physical properties can provide AI systems with the foundation needed to evaluate potential new compounds.

In this emerging model, data, artificial intelligence and laboratory research increasingly complement one another.

A New Technological Tool for the Energy Transition

Artificial intelligence will not eliminate the need for geological exploration, laboratory testing or traditional scientific expertise. Its contribution could instead lie in making these activities more focused.

From identifying potentially valuable deposits to reassessing mining waste and virtually screening new material combinations, AI offers several ways to accelerate research while improving the use of available resources.

As demand for strategic materials continues to evolve alongside the global energy transition, the combination of geological knowledge, materials science and artificial intelligence could therefore become increasingly significant. The development of better databases and more sophisticated analytical models may ultimately help researchers identify not only new sources of existing resources, but also entirely new materials capable of supporting future technologies.