As the United States pushes to reduce its dependence on China for critical minerals, New York-based Radical AI is taking a different approach to securing strategic materials: using artificial intelligence to develop new alloys that require fewer supply-constrained metals.
The company has developed a self-driving laboratory platform that uses AI and robotics to design, manufacture and test advanced materials, an innovation that it says dramatically shortens the discovery process for alloys used in aerospace, defense and other industrial applications.
In September, Radical AI partnered with Sandia National Laboratories to perform research on metal-hydrogen interactions and discover new materials to separate hydrogen from waste gas streams.
Working from its facility at the Brooklyn Navy Yard, Radical AI has demonstrated an alloy discovery process that produced 300 new compositions in approximately 16 weeks, CEO Joseph Frause told MINING.COM in an interview.
The work is backed by RTX and Nvidia and could, the company says, offer US manufacturers another way to address supply chain vulnerabilities by redesigning materials around more readily available elements.
“If you have a system that can design new materials based on the constraints that you give it, and you have existing materials that have real constraints from the supply chain, we can help solve that problem,” Frause said.
“We can design new materials that take into consideration those constraints while still executing on the function or what we call the property set that is required for an application.”
Designing around critical minerals
Much of Washington’s critical minerals strategy has focused on expanding domestic mining, processing and refining capacity to reduce reliance on foreign suppliers.
Radical AI operates further downstream, using refined metals to develop advanced materials that meet specific industrial performance requirements.
The company is not involved in mining or mineral processing. Instead, its technology identifies combinations of elements that can deliver desired material properties while minimizing exposure to supply-constrained metals.
One example is hafnium, a strategically important element used in high-temperature alloys for aerospace and defense applications.
Radical AI says it has successfully developed high-performance alloys designed for extreme-temperature environments that contain very little, and in some cases almost no, hafnium.
“Hafnium is one of those elements in the periodic table that is majorly owned from China’s supply chain and is a risk factor to the Department of War and just the United States and our allies,” Frause said.
The company’s approach allows manufacturers to specify which elements should be excluded from a new material while maintaining the required strength, durability and resistance to heat and oxidation.
“We are mixing up the ingredients together, not messing with the ingredients,” Frause said. “We are using the ingredients that are available. Now, what we can do is decide what ingredients should be on the list.”
The technology could potentially help manufacturers develop alternatives to alloys containing metals vulnerable to export restrictions, geopolitical tensions or limited domestic production.
Cutting years off materials discovery
Traditional mining can take 10 to 15 years to come into operations after the discovery process, and cost more than $100 million for a single material system. (fact check and elaborate)
The lengthy and expensive process has left many aerospace and defense manufacturers relying on alloys developed decades ago.
“A lot of the advanced materials that they use, the structural metals they use, are old. 20, 30, 40, 50 years old in some of the applications,” Frause said.
The company believes autonomous experimentation can substantially shorten those timelines by integrating AI-driven material design with physical testing.
Once the system identifies a promising composition, it designs an experimental campaign to synthesize and evaluate the alloy.
The laboratory then manufactures a physical sample weighing approximately 50 to 200 grams before analyzing its chemical composition, grain structure and mechanical properties.
Testing can include hardness, tensile strength and high-temperature performance, including conditions relevant to hypersonic applications.
The resulting data is fed back into machine-learning models to improve subsequent experiments.
“We’ll take all of that data and we’ll analyze it with machine learning and then we will feed it back into that design loop,” Frause said.
“We call this the active learning loop so that the next time I make a new alloy, I am looking at the past results as I’m doing so.”
By continuously incorporating experimental results into its models, Radical AI aims to accelerate the iterative process traditionally performed by materials scientists.
The longer-term ambition is to reduce materials development timelines from decades to just a few years.
“Put simply, you take a 20-year development timeline and you shrink it down to one, two, three years,” he said.
A new model for aerospace and defense
Beyond speeding up discovery, Radical AI believes its technology could change how manufacturers design products.
Rather than selecting from existing alloys, companies could develop materials specifically engineered for the requirements of new aircraft, engines, defense systems and industrial equipment.
The approach, known as concurrent engineering, would allow materials development to proceed alongside product design.
“As I am designing my new airplane, I am designing the materials that go in my airplane,” Frause said.
“That does not happen in the alloy space today.”
The company argues that developing application-specific alloys could unlock performance improvements that are difficult to achieve using conventional materials.
It could also allow manufacturers to account for critical mineral availability at the earliest stages of product development, rather than confronting supply chain limitations after designs are finalized.
Commercial platform, not alloy production
Founded in January 2024, Radical AI has commercialized its autonomous laboratory platform and says it already has paying customers.
Unlike a conventional metals producer, the company does not manufacture commercial quantities of alloys or enter into offtake agreements.
Instead, customers use its technology to accelerate materials discovery and can retain intellectual property rights to the compositions developed through the platform.
“Our product is the platform, the self-driving lab. That’s what we’ve commercialized and that’s what we do have customers on today,” Frause said. “We don’t off-take the materials.”
The company remains focused on discovery rather than industrial-scale manufacturing, meaning promising alloys must still undergo additional development and qualification before being deployed in commercial applications.
That distinction is particularly important in aerospace and defense, where materials must satisfy stringent safety, performance and reliability requirements.
While AI-driven discovery will not eliminate the need for critical mineral mining and refining, Radical AI believes it can provide manufacturers with greater flexibility in choosing the metals used in advanced applications.
For the mining industry, the emergence of materials substitution technologies could eventually influence demand for certain critical metals, even as new industrial applications create opportunities for others.
“It’s really the way we do science that’s going to impact the entirety of the materials industry at large,” Frause said. “It’s going to be a new way to do scientific discovery.”
Source: https://www.mining.com/radical-ai-uses-autonomous-labs-to-accelerate-alloy-discovery/
