The transition to sustainable energy requires a large amount of essential minerals, and this demand is only expected to increase. By 2050, the demand for minerals such as graphite and cobalt is projected to rise by over 200%, while the demand for lithium is expected to increase by 910% and rare earths by 943%.
Despite the high demand, there are sufficient mineral reserves in the earth’s crust to support the energy transition. However, the exploration and processing of these minerals are heavily concentrated in specific geographical areas, mainly in China. Additionally, discovering and extracting critical minerals, including finding new ones, is a complex and costly process.
To expedite operations, the mining industry for critical minerals has shown growing interest in artificial intelligence (AI). AI has the potential to help in locating new deposits of sought-after minerals and even discovering entirely new materials. Despite a challenging investment market, there has been continuous investment in early-stage AI solutions throughout 2023.
For instance, in March, VerAI, an AI-based mineral asset generator, secured $12 million in Series A funding. In June, GeologicAI raised $20 million for its “core scanning robot” in a Series A round. Later that same month, KoBold Metals, based in Berkeley, raised $195 million with investments from T. Rowe Price, Andreessen Horowitz, and Breakthrough Energy Ventures.
Recently, Google introduced the DeepMind Graph Networks for Materials Exploration, an AI tool for predicting the stability of new materials. According to Google, out of 2.2 million predictions made by GNoME, 380,000 show promise for experimental synthesis, including materials that could lead to future transformative technologies such as superconductors, powerful supercomputers, and advanced batteries for electric vehicles.
The potential role of AI in mineral exploration is vast, and current offerings each take a slightly different approach. For example, GNoME is a graph neural network model trained with data on the structure and chemical stability of crystals. It identifies new minerals with similar structures to known materials, potentially replacing highly demanded minerals like lithium.
On the other hand, KoBold, a favorite among investors, uses machine learning and geological data to predict the locations of mineral deposits below the earth’s surface. Founded in 2018, the company has expanded rapidly by not only offering software but also making strategic investments in land claims and selling mining licenses. KoBold claims to have over 60 mining projects globally.
Other startups utilize machine learning to analyze geological data and identify promising mineral deposits or develop robots capable of scanning and analyzing rock samples.
In the United States, there is a pressing need for clean energy companies to accelerate mineral extraction and processing outside of China, driven by the Biden administration’s guidelines for Inflation Reduction Act tax credits. For instance, automakers must eliminate reliance on critical minerals extracted, processed , or recycled by “foreign entities of concern” by 2025 to qualify for significant benefits.
In response to the Biden administration’s efforts to reduce dependence on China, it is anticipated that China may face a shortage of critical minerals by 2030 or 2035, according to Tom Moerenhout, a research scholar and adjunct professor at Columbia University. While processing capacity can be increased relatively quickly, exploration and other upstream activities typically progress slowly, averaging 12.5 years, as per the International Energy Agency.
The majority of untapped domestic deposits in the US are located near or within Native American reservations, with 97% of nickel, 89% of copper, and 79% of lithium reserves in these areas. However, companies encounter opposition to mine development due to cultural and environmental concerns. For example, in Arizona, mining company Rio Tinto has faced a decade-long dispute over a copper deposit under an Apache religious site.
Due to the challenges related to obtaining permits and complying with legal regulations for these deposits, Moerenhout mentioned that there have been discussions at the national level about initiating “another extensive exploration round, beginning with areas that are much easier to permit and have fewer environmental and social implications than current projects.”
This implies that the US must discover new mineral deposits, and do so quickly. Although there have not been significant technological advancements in mineral exploration for many years, Moerenhout noted that AI has been a major focus for the past few years, especially among smaller ” junior miners” concentrating on a specific mineral.
For these junior miners, he explained that the potential for AI-driven mineral discoveries is enormous. Traditional exploration is a multibillion-dollar endeavor that often does not yield immediate returns.
Moerenhout stated that AI could reduce the exploration timeline and risk, ultimately lowering the cost. In the case of GNoME, the technology could enable miners to target higher-quality ore, facilitating easier production and processing.
“All of this is still in the testing and development phase,” he added. “But if this type of technology can be developed, it could potentially overcome some of the challenges associated with exploration. The potential is significant.”
Additionally, the failed battery start-up Britishvolt’s site in Nothumberland, intended for a gigafactory, will reportedly be acquired by the US private equity firm Blackstone, which plans to repurpose the site for a data center.
Britishvolt was once seen as a leading British green energy innovator, aiming to construct a £3.8 billion car battery factory and create 3,000 jobs.
However, the company collapsed in January 2023 due to overspending or lack of government support, depending on who you ask.
Reportedly, Blackstone intends to develop a hyperscale data center campus on the site, taking advantage of access to affordable renewable energy from offshore wind.
This serves as a powerful metaphor. Although there is a pressing need for more battery capacity in the western world, attention has already shifted to AI.
The overall impact of artificial intelligence and its increasing integration into everyday life is uncertain. However, one thing is certain – AI consumes a significant amount of power and data. Commodity traders anticipate a substantial increase in demand for copper as a result of the AI revolution.
Furthermore, data centers require more than just copper; they also need chips. The 2022 US CHIPS and Science Act has already spurred investment in chip production capacity.
The current surge in chip demand is drawing attention to various niche minerals, many of which are predominantly produced in China.
Tin, for instance, is a beneficiary of the chip boom, as nearly half of all tin is used as solder in circuit boards.
Tantalum, used in capacitors, is another mineral needed by data centers. It is exported from East Africa through complex trade routes that often lead back to artisanal mines controlled by rebels in the eastern DRC.
Additionally, rare earths such as neodymium and yttrium find their way into data centers, used in drive boards and superconductors, respectively.
Renewables demand is expected to increase even further as AI is extremely energy-intensive, with data centers already consuming about 1-1.5% of global electricity production, and this demand is projected to rise as capacity expands.
Increased demand for electricity further strengthens the positive outlook for minerals in the energy transition. The rise in electricity prices will help in the expansion of renewable energy. Wind farms require copper and rare earths, while solar panels need silver, cadmium, and selenium.
The increase in power demand, whether from renewable sources or fossil fuels, will create a need for copper and aluminum for transmission.
The impact of the AI boom that is often overlooked is its potential to utilize stranded electricity. Aluminum production, in particular, heavily relies on inexpensive electricity, leading to the movement of production to regions with low electricity costs, especially remote areas with limited transmission capabilities.
For example, Iceland has effectively exported its abundant geothermal electricity to the world through aluminum smelting. This trend can also be observed in Norway, Saudi Arabia, Bahrain, and remote areas of Russia with access to hydroelectric power.
In recent years, China has become a major player in the global aluminum market, supported by industrial policies and benefiting from cheap electricity from coal and hydroelectric power.
The growing demand for data centers is changing this dynamic. High-speed fiber optic cables can connect data centers in remote areas with affordable electricity, enabling them to export data rather than power.
If the demand for AI continues to rise, the issue of stranded power may become a thing of the past, leading to higher electricity prices in remote locations worldwide and potentially reducing margins for many aluminum producers, despite the increasing demand for the metal.
There is also another aspect to the AI demand puzzle: its potential impact on supply. According to Dutch bank ING, artificial intelligence could assist in meeting the rising demand for critical minerals by aiding the mining industry in discovering new deposits.
“AI, machine learning, and data analytics could be utilized in the discovery and extraction processes to meet the increasing demand for these minerals,” ING stated. However, this would require increased investment in the sector and the willingness of mining companies to adopt new technology.
Although the potential increase in mineral demand, coupled with the assistance of AI in boosting discoveries and refining processes, may seem like good news for miners, it is important to exercise caution.
Investors should remember instances like the old Britishvolt site in Northumbria, which demonstrate the flightiness of capital. The substantial expansion of data centers will also require considerable capital, potentially diverting funds away from the mining industry, which is in dire need of investment.
Historical cycles have shown that mining often struggles to attract sufficient capital, especially after the tech sector has secured its share.
The recent energy boom has proven that optimistic projections for mineral demand alone are insufficient to drive the development of new mines.
How AI is aiding in the discovery of valuable mineral deposits
A metals company based in California, by prominent figures like Bill Gates and Jeff Bezos, has utilized AI to identify one of the largest copper mines backed globally.
Quartz noted that while the association of Bill Gates, Jeff Bezos, and AI may not immediately evoke the image of a massive copper mine in Zambia, the increasing reliance on electric power will necessitate a significant amount of batteries, motors, and wires. This will lead to a high demand for cobalt, copper, lithium, and nickel, creating favorable conditions for prospectors, especially those aiming to enhance the efficiency of their profession.
According to The Economist, KoBold Metals, named after underground sprites from medieval Germany, uses AI to analyze historical geological records and create a “Google Maps” of the Earth’s crust.
The Economist mentioned that while some of the geological, geochemical, and geophysical data required for AI analysis is new, a significant amount was previously stored in national geological surveys, geological journals, and other historical repositories.
Algorithms are then used to “identify patterns and make inferences about potential mining sites,” as reported by the publication. Mining.com highlighted that this technology can uncover resources that traditional geologists may have overlooked and assist miners in determining where to acquire land drill .
KoBold is not the only mining company employing AI, but its significant discovery in Zambia marks a pivotal moment in demonstrating the potential of technology in exploration.
There is a sample room for improvement in AI
AI is increasingly being promoted as a valuable method for discovering new sources of lithium, cobalt, copper, and nickel “more efficiently and with potentially less environmental impact than previous methods”, Business Green reported.
The International Energy Agency has stated that access to these minerals, as well as the necessary investments to obtain more, “do not meet the requirements for transforming the energy sector”.
Copper, in particular, is utilized in solar panels, wind turbines, and other equipment essential for transitioning the world to net-zero energy. “So, if AI has the potential to extract critical minerals from the ground and into products more rapidly, that could be beneficial,” Quartz noted.
The world’s largest mining companies are facing challenges in finding high-quality assets, and the demand for copper is “expected to surge as countries strive to electrify their transportation systems and shift to renewable energy,” according to the Financial Times (FT).
The recent discovery in Zambia offers a “potential boost to the efforts in the west to reduce its dependence on China for metals crucial to decarbonizing everything from vehicles to power transmission systems”.
Up to 99% of exploration projects fail to materialize into physical mines. “AI, therefore, has a lot of room for improvement,” as stated significantly by The Economist. “It may also assist with a more nuanced issue. By expanding the amount of rock that can be explored, it will enable new discoveries in familiar, well-governed countries.”
Josh Goldman, founder and president of KoBold Metals, said to the FT: “Exploration is where babies come from. You can help babies grow but you’ve got to get the birth rate up. That’s the hardest part: how do you find things in the first place.”
It appears that AI could offer a solution
Researchers at the China University of Geosciences in Wuhan utilized artificial intelligence (AI) to identify deposits of rare earth minerals and identified a significant potential reserve in the Tibetan plateau in the Himalayas, according to the South China Morning Post.
In the past, China held a dominant position in mining bulk minerals such as copper, iron, aluminum, and coal, which fueled its industrial and urban growth. However, the evolving landscape of technology now necessitates the use of rare earth minerals for various applications spanning from energy to defense.
Since rare earth resources exist in countries other than China, China’s dominance has been waning. Reserves discovered in Inner Mongolia have become a major production zone for China. nevertheless, the accidental discovery of lithium in some rock samples from Tibet nearly a decade ago provided hope that could shift the balance in China’s favor once again.
Turning to AI
Geologists in China have long studied the Himalayan belt for minerals but only found granite in locations, including Mount Everest. Two years ago, a team of researchers led by Zuo Renguang at the China University of Geosciences developed an AI-based system to analyze raw satellite data to identify new rare earth deposits.
The AI was trained on a limited data set to recognize light-colored granite that could contain rare-earth minerals such as niobium and tantalum alongside lithium, a crucial component for manufacturing electric vehicles.
However, the team worked on enhancing the accuracy of its algorithms by incorporating information about the chemical composition of rocks, their magnetic and Initially electrical properties, and geological maps of the region, resulting in an increased accuracy rate of 96 percent.
Mining in the Himalayas
The mineral reserves identified by the machine are estimated to be at least the size of the site in Mongolia, if not larger. However, mining in the Himalayas is not as straightforward as in Inner Mongolia.
For one, the reserves are located in the Tibetan belt of the country, where there is a commitment to protecting the environment. The Himalayan belt extends into countries such as India, Nepal, and Bhutan and holds strategic significance.
Activities like mining contribute to economic growth and draw more people, but some areas are contested territories and could escalate geopolitical tensions.
From China’s perspective, the regions are also remote and will require additional investments in infrastructure to make them accessible while also managing waste from the operations, as reported by the SCMP. In an area with limited water resources, poorly managed activities could have serious repercussions.
Chinese researchers are not the only ones utilizing AI to locate lithium, nickel, cobalt, and copper deposits. KoBold, a mining company based in Berkeley, has adopted this approach and operates at 60 sites across three continents.
The company is backed by venture capitalist firm Andreessen Horowitz. A recent round of funding received support from Bill Gates’ VC firm Breakthrough Energy Ventures and achieved a valuation of one billion dollars, as reported by Fortune.
US Critical Materials Corp. has announced the signing of a definitive agreement with VerAI Discoveries Inc., a company that uses artificial intelligence (AI) to generate mineral discoveries, to deploy its AI-Powered Mineral Targeting Platform.
This technology increases the likelihood of detecting minerals under covered terrain and reduces surface disturbances at US Critical Materials’ Sheep Creek rare earths properties in Montana, USA.
The exploration partnership between US Critical Materials and VerAI uses top-notch technology to explore the covered terrain at the Sheep Creek Area of Interest (AOI), significantly boosting the likelihood of success by 100 times compared to industry benchmarks. This AOI has a rich geological landscape, confirmed by Idaho National Laboratory and independent geophysical surveys.
With VerAI’s AI-powered mineral targeting technology, US Critical Materials aims to establish new industry standards for environmentally conscious mineral exploration activities, offering the opportunity to bring rare earth elements to the market in their purest form, crucial for the green energy transition.
Jim Hedrick, President of US Critical Materials, and former rare earths commodity specialist for the US Geological Survey (USGS), mentioned, “The addition of this AI/ML technology will enhance US Critical Materials’ current exploration methodologies. We are excited to have signed a definitive agreement with VerAI Discoveries to utilize its next-generation AI technology and unique capabilities to discover high-probability targets under covered terrain.”
Hedrick added, “AI-assisted mineral exploration platforms are gaining recognition in the mining industry and major media outlets. The Defense Advanced Research Projects Agency (DARPA) is also exploring AI-assisted mining to expedite the search for critical minerals needed for US industry, consumer use, and, most importantly, the US military.”
US Critical Materials’ latest samples indicate total rare earth elements (TREE) readings up to 20.1%, with combined neodymium praseodymium up to 3.3%. The company also has gallium readings as high as 490 ppm (parts per million). Gallium is profitable to produce at 50 ppm. The company believes there is a substantial tonnage at Sheep Creek and expects to discover more high-grade critical mineral locations using VerAI’s innovative, artificial intelligence technology.
“VerAI is leading a paradigm shift in the exploration sector. We believe that AI and machine learning are essential tools for revolutionizing mineral exploration,” said Yair Frastai, CEO of VerAI Discoveries. “With this definitive agreement, US Critical Materials is proving its forward -thinking approach by leveraging our advanced AI-based targeting technology to systematically de-risk the economics of discovering concealed mineral deposits.”
Both companies acknowledge the commitment and responsibility to protect all aspects of the environment in the Bitterroot Valley.
A Singapore-based startup is using artificial intelligence (AI) to search for reserves of critical minerals, betting that the technology can help reduce the cost and time spent in mining.
Atomics, the firm, has employed gravity and AI to develop a “virtual drill” technology known as Gravio that can define ore bodies and enhance the efficiency of minerals projects.
Drilling a single hole to search for a mineral can cost from $7,000 to $33,000. A lithium miner might need as many as 400 holes to prove up a resource, so building a more accurate virtual picture before drilling can reduce costs.
“The key challenge is that sometimes (drill holes) don’t actually hit the reserve,” said Atomionics CEO Sahil Tapiawala.
The company aims to decrease these “empty” samples by at least half, he added.
Like many exploration techniques, Atomionics uses the gravity signatures of different minerals to pinpoint where they lie beneath the earth.
It is able to do so more precisely than typical air-based survey techniques and processes data in real time using AI, speeding up the work of defining ore bodies, Tapiawala said.
The mining industry employs various techniques to find minerals, including ground-penetrating radar and aeromagnetic surveys, but no one method guarantees success.
KoBold Metals, a California-based startup backed by billionaires Bill Gates and Jeff Bezos, is also utilizing AI to search for metals such as lithium.
“The energy industry would traditionally defer to seismic data before undertaking any drilling project,” stated Cameron Fink, Bridgeport Energy exploration manager.
“With further development, Gravio can present as a low-cost alternative to traditional methods of exploration.”
First customers in Australia, US
Atomionics has secured agreements with three major mining companies as part of a plan to locate metal ore deposits crucial to the energy transition, according to Tapiawala.
This will complement the firm’s existing work in Queensland with New Hope’s Bridgeport Energy division.
The mining giants are anticipating to complete data collection and analysis using Gravio by early next year.
“We are actively implementing our technology for vital minerals, specifically copper, nickel, and zinc,” Tapiawala stated, noting that the technology is being introduced in Australia and the US.
He chose not to disclose the names of the miners due to commercial confidentiality reasons. The privately-held company is supported by various Singapore-based government agencies and strategic investors.
Critical minerals company signs definitive agreement with VerAI to explore high-grade project in Montana
US Critical Materials Corp. has signed a definitive agreement with VerAI Discoveries Inc. to utilize VerAI’s AI-powered mineral targeting platform for the exploration of high-grade rare earths and gallium at the Sheep Creek project in southwestern Montana. This partnership builds upon the AI -powered critical minerals exploration collaboration announced in May.
VerAI’s AI and machine learning technology assists geologists in locating hidden mineral deposits with greater accuracy by processing geophysical and other exploration-related data.
VerAI Discoveries CEO Yair Frastai stated that AI and machine learning are essential tools for revolutionizing mineral exploration and are spearheading a paradigm shift in the exploration sector.
The use of AI technology for identifying buried mineral targets ahead of drilling can accelerate the mineral discovery process, reduce costs, and minimize environmental impact.
VerAI Discoveries COO Amitai Axelrod highlighted that their discovery process primarily occurs in the data space, significantly reducing the environmental footprint compared to traditional methods. This approach minimizes disturbance to ecosystems and local communities by avoiding extensive drilling and physical access to remote areas.
US Critical Materials plans to utilize VerAI’s technology to set new industry standards for environmentally conscious mineral exploration and aims to establish a domestic source of rare earths, gallium, and other critical minerals found at Sheep Creek.
US Critical Materials President Jim Hedrick, a former rare earths commodity specialist for the US Geological Survey (USGS), stated that the addition of AI/ML technology will enhance the company’s current exploration methodologies.
The AI-assisted exploration at Sheep Creek could help establish this southwestern Montana project as an important domestic source of critical minerals essential to America’s economy and national security.
Samples collected from Sheep Creek contain high grades of rare earths, including neodymium, and praseodymium, crucial for electric vehicle motors, with grades as high as 20.1% total rare earths.
Gallium, used in semiconductor production, is found alongside the rare earths at Sheep Creek, with samples containing as much as 490 parts per million gallium, indicating the project’s potential to become the highest-grade source of gallium in the US
According to the USGS, China supplied the majority of the world’s gallium and rare earths in 2023, highlighting the significance of developing domestic sources for these critical minerals.
The same rocks hosting rare earths and gallium at Sheep Creek also contain niobium, scandium, and yttrium, all considered critical to the US
US Critical Materials has finalized a deal to leverage VerAI’s mineral targeting technology to accelerate the discovery of concealed mineralization at Sheep Creek, aiming to provide a domestic alternative to China for rare earths, gallium, and other critical minerals.
Earth AI, a clean energy metals explorer, has announced the first discovery of a greenfield molybdenum deposit using artificial intelligence near Armidale, New South Wales, Australia. The land is free and unlicensed, previously believed to be barren.
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But the founder and CEO of Earth AI, Roman Teslyuk, and his team had a feeling. As a result, they made the decision to create a series of hypotheses and methodically test them. Each hole they drilled tested a single hypothesis.
After eight months and the loss of much equipment to snow, four holes were drilled under winter conditions in the high Australian plateau, and they were able to pinpoint high-grade ore.
“Before this, we drilled four holes in the Northern Territory, which brings us to a success rate of one in eight at discovering economic grade mineralization. This is a significant improvement over the industry standard of one in 200,” Teslyuk told Mining.com .
MDC: Can you provide more details on how the discovery happened?
Teslyuk: Our Mineral Targeting Platform is a geological deep learning solution that excels at finding mineral systems using surrounding geological and geophysical data. It is trained on virtually all known mineral prospects across the continent and, using this knowledge, predicts new systems.
In this instance, we had a “promising target” on land that had been explored four times previously by junior explorers and major companies. However, despite the substantial amount of money spent on exploration, no mineral deposits were found.
But we were committed, licensed the area, consulted with the community, obtained all the permits, and began exploring. We discovered high-grade molybdenum. The observed grades are 1.5-2 times higher than the world’s leading molybdenum mines.
High molybdenum grades were confirmed in three samples analyzed by a certified laboratory. These grades, registered at 0.3%, 0.26%, and 0.135%, exceed the currently mined grades of 0.16% and 0.14% found in the world’s leading molybdenum mines, Climax and Henderson. Both of these mines are owned by Freeport McMoran.
As a high-performance explorer of clean energy minerals, we don’t focus solely on one element during our exploration. This is because deposits usually contain multiple metals. We analyze the mineral system to understand which metals are likely to form an economic deposit, but also indirectly track other critical metals like copper, tin, tungsten, and gold that might form adjacent deposits or be mined as a secondary commodity.
In this case, we also intersected low-grade copper at 0.3% adjacent to high-grade molybdenum mineralization.
MDC: Earth AI mentions using modular drilling. Can you explain this?
Teslyuk: Modular drilling, also known as responsible drilling, refers to our innovative approach to mineral exploration drilling, which embraces modularity as crucial for redundancy and operational efficiency. It is a drilling hardware system designed by Earth AI to be self-sufficient, minimize environmental impacts, and ensure a safe, efficient drilling operation in the most remote desert environments.
Our modular hardware eliminated the need for groundwork by design. Our onboard waste management system ensures the safe treatment and disposal of drilling waste. Modular drilling also enables significant logistical gains, as we can carry more stock in a highly organized manner, we come more prepared , and our operation can remain self-sufficient no matter what drilling challenge we encounter.
MDC: Can you describe how the AI system used to find the greenfield deposit works?
Teslyuk: It is helpful to understand how our entire process system works, which consists of three phases: Targeting, hypothesis, and drilling.
Our AI system is employed in the foundation of our exploration, the targeting phase – our models train on millions of geological cases from the entire continent and have learned to identify areas of mineralization and highlight locations with a high probability of finding a mineralized system. We deploy teams into the field to sample and review the targets.
In the hypothesis phase, geologists are on the ground studying the mineral system. At this stage, a sister technology is utilized that helps them better understand the geological setting and aids them in forming hypotheses.
The drilling phase is where we test our hypotheses by drilling down to a depth of 600 meters and proving or disproving the presence of mineralization. Each drill hole provides invaluable knowledge that is then fed back into the system and used to form new hypotheses.
As a result of this process, our AI prediction tools are the most accurate in the industry.
MDC: What baseline data are fed to this AI system?
Teslyuk: It is trained on a vast amount of data – 400 million geological cases from across the continent. The fundamental datasets for learning are remote sensing, geophysical, and geochemical datasets.
MDC: How is your AI system different from others?
Teslyuk: Geoscience is a new domain for AI, and our AI system is unique in its approach as it thinks like a geologist. The unique aspect lies in how we teach the AI to learn geology. To do this, you need to be both a geology and AI expert, a skillset that is incredibly rare.
Another important aspect of the Mineral Targeting Platform is the focus on re-learning the archive data at the continental scale.
Geoscientists are motivated to produce papers, resulting in the generation of increasingly detailed but unconnected data sets, with no incentives for drawing conclusions. This challenging task may not lead to any significant outcomes.
The unique feature of our technology is its ability to predict mineral systems with extremely low detection limits. This capability is highly valuable given that all easily accessible resources have been depleted, and traditional regional targeting tools are unable to solve this issue.
For instance, in the case of the molybdenum porphyry, a 0.3% molybdenum mineralization soil anomaly with a detection limit of 0.002% was observed at the surface.
AI Develops Revolutionary Magnet Without Rare-Earth Metals in Just 3 Months
There is an immediate need to transition away from fossil fuels, but the adoption of electric vehicles and other green technologies can create environmental pressures of their own. This pressure could be alleviated by a new magnet design, free from rare-earth metals, developed using AI in only three months.
Rare-earth metals are vital components in modern gadgets and electric technology, such as cars, wind turbines, and solar panels. However, extracting these metals from the ground comes with significant costs in terms of finances, energy, and environmental impact.
As a result, technology that does not rely on these metals can accelerate the transition towards a greener future. Enter Materials Nexus, a UK-based company that utilized its custom AI platform to create MagNex, a permanent magnet that does not require rare-earth metals.
While this is not the first magnet of its kind to be developed, discovering such materials typically involves extensive trial and error and can take decades. The use of AI accelerated the process by approximately 200 times – the new magnet was designed, synthesized, and tested in just three months.
The AI evaluates over 100 million compositions of potential rare-earth-free magnets, considering not only their potential performance but also supply chain security, manufacturing costs, and environmental impact.
Physicist Jonathan Bean, CEO of Materials Nexus, anticipates that “AI-powered materials design will not only impact magnetics but also the entire field of materials science.”
Materials Nexus collaborated with a team from the University of Sheffield’s Henry Royce Institute in the UK to produce the magnet. It is believed that similar techniques could be employed to develop other devices and components that do not rely on rare-earth magnets.
According to the creators of MagNex, the material costs are 20 percent of what they would be for conventional magnets, and there is also a 70 percent reduction in material carbon emissions.
In the electric vehicle industry alone, the demand for rare-earth magnets is expected to be ten times the current level by 2030, according to Materials Nexus, underscoring the potential significance of these alternative materials.
In addition to using AI to enhance manufacturing processes, researchers are actively seeking more sustainable methods for obtaining rare-earth materials. Breakthroughs like this should expedite the shift away from fossil fuels and carbon emissions.
Of course, the AI industry itself faces challenges in terms of carbon emissions. If its carbon footprint can be managed, AI could prove to be a valuable tool in the transition to green technology.
“This accomplishes demonstrate the promising future of materials and manufacturing,” states materials scientist Iain Todd from the University of Sheffield.
“Unlocking the next generation of materials through the power of AI holds great promise for research, industry, and our planet.”
Embracing AI: Revolutionizing sustainable mining and mineral exploration in Saudi Arabia
As Saudi Arabia explores the integration of AI in mineral exploration, it marks a significant milestone not only for prosperity economic but also for upholding its commitment to environmental sustainability.
Saudi Arabia is at a crucial juncture, aiming to diversify its economy by leveraging the abundant mineral wealth beneath its soil. This shift aligns with Saudi Vision 2030, which promotes sustainable development and a technology-driven future, ushering in a new era of economic diversification .
Traditionally, mineral exploration has relied on extensive fieldwork, geophysical surveys, and geological analysis. However, this landscape is rapidly evolving globally, including in Saudi Arabia, driven by Artificial Intelligence (AI).
One of the most influential innovations, AI is transforming industries worldwide, including the mining sector, by reshaping technological interactions. AI improves mineral exploration and environmental protection in various ways:
AI’s ability to process and analyze extensive datasets, including geological, geophysical, and geochemical data, satellite imagery, and historical exploration records, makes it a leader in adopting safer, more efficient, and environmentally friendly mineral exploration practices.
Machine learning models in AI can identify patterns, anomalies, and potential mineral deposits that traditional methods often miss, providing precise forecasts and detection of mineral availability, reducing unnecessary exploratory drilling and preserving the environment.
AI plays a crucial role in integrating advanced technologies such as drones, robotics, and autonomous systems into mineral exploration, replacing traditional, labor-intensive methods by rapidly analyzing large datasets to locate mineral deposits accurately.
Satellite imagery, processed by AI, plays a crucial role in improving mining operations and environmental management, providing detailed insights into site conditions, vegetation cover, and topography, essential for planning and managing mining activities.
AI-driven solutions improve safety by reducing human errors and ensuring compliance with the highest safety standards, minimizing the environmental impact of exploration and aligning with global sustainability goals.
AI’s impact on mining promises to transcend today’s applications, shaping paths we have yet to fully imagine. Imagine a world where AI not only enhances mineral extraction but also pioneers the creation of self-sustaining, closed-loop ecosystems within mining sites.
Deep sea mining, emerging as a significant new frontier, stands to benefit immensely from AI technologies, optimizing the mapping of seabed minerals, automating submersible operations, and monitoring environmental impacts.
AI acts as a force multiplier, enhancing human capabilities and enabling more informed decision-making based on data-driven insights, fostering collaboration and innovation. As the Kingdom of Saudi Arabia continues to explore the integration of AI in mineral exploration, it opens a promising new chapter not only for economic prosperity but also for upholding its commitment to environmental sustainability.
Rare minerals occur in a wide variety of deposits across the Earth. Their demand has grown rapidly, but they occur in limited minable deposits. Conventional technology allows searching for rare minerals using geochemical exploration as the main method. X-ray fluorescence (XRF) is a very useful instrument for real-time qualitative and quantitative evaluation of rare minerals.
Nevertheless, it is often challenging to predict the presence of minerals and mineral-forming locations due to the complex interactions between geological, chemical, and biological systems in nature.
Scientists are actively seeking new technologies to identify mineral deposits more easily, as doing so can improve our understanding of Earth’s history and help meet industrial demands.
Hunting for Valuable Minerals
Mineralogist Shaunna Morrison and geoinformatics scientist Anirudh Prabhu have developed a machine learning model based on artificial intelligence (AI) that has the potential to identify specific mineral occurrences.
In collaboration with their research colleagues, they utilized data from the Mineral Evolution Database to predict previously unknown mineral occurrences.
The database contains information on 295,583 locations of 5,478 mineral compounds, and the model used patterns based on association rules, which are a result of Earth’s dynamic evolutionary history.
To test the efficiency of their AI-based model, the researchers explored the Tecopa basin in the Mojave Desert in eastern California, known for its Mars-like geographic conditions.
Following their exploration, the machine learning model successfully predicted the presence of important minerals such as rutherfordine, bayleyite, and zippeite, as well as deposits of critical rare earth elements like monazite-(Ce), allanite-(Ce), and spodumene.
The study’s findings demonstrate the effectiveness of mineral association analysis as a predictive tool, which could benefit mineralogists, economic geologists, and planetary scientists. The researchers hope that this analysis will enhance our understanding of mineralization on Earth and in the broader Solar System.
Exploring the Past Through Minerals
According to the American Museum of Natural History, the Earth is home to 5,000 mineral species. Minerals not only serve as raw materials for industry, but they also provide the oldest surviving records of our Solar System’s formation and evolution.
They serve as enduring evidence of geological events and ancient terrains. Understanding how minerals have changed over time can help experts unravel the history of our planet.
The International Mineralogical Association (IMA) has established a standard for classifying minerals based on their composition and structure. Categorizing minerals by origin using the IMA’s system can provide valuable insights into Earth and other planets.
The role of minerals in the scientific community goes beyond tracing Earth’s past; they also play a crucial part in current activities on our planet. The Earth’s interior dynamics are reflected in tectonic events such as volcanic eruptions and earthquakes. Chemically zoned minerals are essential for understanding These catastrophic events.
Scientists Propose a New Approach for Locating Rare Earth Deposits
A team of geologists and materials scientists from the University of Erlangen-Nuremberg in Erlangen, Germany, has developed a new method for identifying untapped rare earth deposits.
The researchers suggest that despite the name “rare earth metals,” these materials are actually relatively evenly distributed worldwide. However, not all deposits are economically viable or easily extractable, leading them to propose a new technique for locating these deposits.
The researchers explain their new technique for finding rare earth metals in an article titled “Cumulate olivine: A novel host for heavy rare earth element mineralization,” published in the journal Geology.
Finding Rare Earth Metals in Igneous Rocks
Researchers have analyzed rock samples from the Vergenoeg fluorite mine in South Africa, where they discovered fayalite crystals – an iron-rich member of the olivine mineral group – deposited in granite-like magma sediments, potentially containing significant amounts of heavy rare earth elements. Fayalite is a reddish-brown to black rock mainly used as a gemstone and for sandblasting processes.
This mineral is found worldwide, primarily in igneous rocks resulting from volcanic activity and abyssal rocks formed deep in the crust.
The researchers also explained that olivine, the mineral class to which fayalite belongs, and its rare earth element systematics are not well understood. Using atom probe tomography maps, researchers confirmed the highest concentrations of heavy rare earth elements in the crystal lattice of fayalite, with lithium traces acting as the main charge balancer in the chemical structure.
Furthermore, the German team utilized laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) – a sophisticated analytical technique that employs micro-sampling to deliver precise elemental analysis of solid materials – to identify that the cumulate fayalite in the Paleoproterozoic Vergenoeg F -Fe-REE site in South Africa contains the highest recorded rare earth element (REE) contents, indicating a heavy rare earth element (HREE) enrichment at approximately 6000 times the chondritic values.
Dr. Reiner Klemd from the Geozentrum Nordbayern at the University of Erlangen-Nuremberg emphasized the significance of the discovery of fayalite as a new potential source for identifying new rare earth element deposits, particularly due to the increasing scarcity of heavy rare earth elements on the global market.
Rare Earth Elements
The elements known as rare earth elements or rare earth metals are part of a group of 17 heavy metals with similar structures. According to the American Geosciences Institute, this group includes the fifteen lanthanides on the periodic table, as well as yttrium and scandium.
Additionally, the US Geological Survey explains that these rare earth elements are essential in various applications, including high-tech consumer electronics, defense, navigation and communication systems, and more.
It was also reported that in 1993, 38 percent of the world’s rare earth element supplies came from China, 33 percent from the United States, 12 percent from Australia, and five percent each from India and Malaysia. However, by 2011, China had already accounted for 97 percent of the world’s rare earth element supplies.
For a technology to make a significant impact on the mining industry, it must be capable of greatly enhancing the speed of execution and process efficiency, from exploration all the way through production and reclamation.
Deloitte states that Artificial Intelligence (AI) is a developing suite of advanced and practical technologies that empowers mining companies to evolve into insight-driven enterprises that utilize data to derive key advantages.
AI-powered systems utilize various algorithms to arrange and comprehend vast amounts of data, with the aim of assisting miners in making optimal decisions. AI’s immediate application in mining is particularly useful during the prospecting phase, especially for uncovering deposits.
The traditional method of discovering the world’s next copper or gold deposit relies more on art than science, revolving around outdated technologies that provide incomplete or conflicting data. This generates inefficiencies that contradict the principles of mining and causes unnecessary disruptions for the global supply chain.
AI systems, however, can ingest and analyze diverse data to help miners gain a better understanding of the environment and the terrain, bringing them closer to potential discoveries.
AI technology can identify the precise locations of hidden mineral deposits, particularly in underexplored regions of the world, in a fraction of the time and with significantly greater accuracy.
About VerAI
VerAI Discoveries is dedicated to accelerating the global zero-carbon transformation by uncovering the minerals essential for our sustainable future.
VerAI employs an innovative AI targeting platform that detects concealed mineral deposits in covered terrain, while continuously enhancing the probabilities of success and reducing the time to discovery.
Headquartered in Boston and operating in both North and South America, VerAI generates multiple high-probability target portfolios in select jurisdictions and collaborates with leading exploration companies to create long-term value by discovering new mineral deposits.
Its board of directors, advisors, and technical team possess decades of experience in the mineral exploration and AI sectors.
VerAI is supported by two venture capital funds: Chrysalix Venture Capital, which includes strategic investors such as Teck Resources, South32, Caterpillar, and Shell, and specializes in mining transformation innovation; and Blumberg Capital, experienced in applying AI solutions to disrupt various traditional industries .
VerAI Methodology
VerAI utilizes high-resolution geophysics data as the primary data source for generating its targets. The data covers an area of approximately 170 km north-south by 60 km east-west, mainly situated over the Paleocene (or Central) mineral belt in northern Chile , but also partially encompassing portions of the Coastal mineral belt.
The study block extends from just south of the multi-million ounce El Peñon gold-silver mining district (Yamana Gold) in the north, to the Franke copper mine (KGHM) in the south. It also includes several historic mines and exploration projects, as well as the operating Guanaco and Amancaya mines (Austral Gold).
The AI targeting process is multi-faceted and iterative, enhancing the confidence that the generated targets are narrowed down to the very best matches to be staked and claimed in northern Chile.
The targets are mostly concealed by post-mineral, gravel-filled basins, or “pampas” where the underlying geology of interest is largely not visible or available for geologic mapping, resulting in targets that have eluded previous exploration campaigns.
Conclusion
After years of insufficient investment, we are finally beginning to confront the reality of having inadequate copper for our future needs.
It is estimated that at least 20Mt of copper supply must be developed in the next two decades, equivalent to one large million-tonne mine (ie, an Escondida) every year from now on.
The solution, as straightforward as it may seem, is to have more copper projects that can be developed into producing mines.
The next major copper mine(s) are likely to be discovered in Chile, the world’s top producer by far, which is why we have been monitoring the progress of Pampa Metals closely over the past year.
With one of the largest prospective property packages along the mineral belts of northern Chile, the company stands to take advantage of some areas that major miners may have overlooked while conducting brownfield exploration on the peripheries of existing mines.
Drilling conducted so far has already indicated signs of a fertile porphyry system, which will undoubtedly be followed up by more drilling and positive results.
And the recent agreement with VerAI, supported by a technology that has demonstrated success in locating mineral deposits, could expand Pampa’s dominant land position even further.
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