Radical AI

The AI materials scientist

March 30th, 2026

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From replacing stone with bronze to turning raw sand into silicon chips, materials science has always been the invisible backbone of humanity’s greatest technological leaps. Yet, for all its transformative power, the process of developing these innovations remains remarkably inefficient. The typical path to developing a novel material requires scientists to first understand the underlying chemistry and physics, then determine the process of manufacturing them at scale, and finally put them through rigorous stress testing. That process routinely lasts up to 20 years, burning through extensive R&D funding.

Radical AI is rethinking the scientific method by building a closed-loop system for material discovery and development, where AI designs materials, tests them, and learns from the results in a continuous feedback cycle. Specifically, it uses a process called inverse design, where it starts from a target property, such as thermal resistance from a hypersonic vehicle. Then, using machine learning, it works backwards to reduce the enormous compositional search space to the most promising candidate alloys. Finally, its largely autonomous, self-driving lab synthesizes and validates those materials. The resulting experimental data is captured and fed directly back into its models so the AI scientist can learn in real time from each experiment, continuously refining its intuition and accelerating the next cycle of discovery.

Radical’s goal is not only to make discoveries in materials science but also to build breakthrough materials that could revolutionize industries, such as novel alloys for hypersonic aircraft, reusable space systems, and next‑generation nuclear fusion reactors.

Check it out: radical-ai.com

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Radical is a full-stack materials company discovering high value, application-specific materials and monetizing them by either selling proprietary materials/components or selectively licensing the underlying technology to partners. Its primary customers include defense contractors, semiconductor fabs, hypersonic flight companies, and nuclear fusion companies that demand niche materials. 

As a materials science PhD candidate at Rice University and researcher at the US Army Research Lab, Radical’s CEO Joseph Krause experienced firsthand how slow, fragmented, and manual materials R&D is. As an investor at AlleyCorp in 2022, he watched as AI continued to transform industries while material science remained largely untouched, despite being the critical bottleneck for frontier technologies. 

While brainstorming with co-founder Jorge Colindres, fellow investor at AlleyCorp, the two concluded that the real opportunity lay in automating the discovery and commercialization of new materials with a fully integrated, AI-native approach. According to Krause, self-driving labs were already proving their value in biopharma, and it was clear that materials science needed the same. Knowing this, Krause and Colindres flew to California to meet the person who would become their future co-founder, Gerbrand Ceder. Cedar leads the Computational and Experimental Design of Emerging Materials Research Group at UC Berkeley and has been actively building an AI-driven materials science lab. The three quickly realized they shared the same conviction that the shift was inevitable and decided it was time to build Radical.

Radical AI's customers span four high-value verticals, each with significant materials demand. The global semiconductor materials market was valued at roughly $72 billion in 2025 and is projected to reach about $104 billion by 2034, with the compound semiconductor sub-segment estimated at $67.69 billion. The aerospace and defense materials market is expected to reach $49 billion by 2035, and the broader hypersonic technology market, where thermal protection materials remain a critical bottleneck, was valued at $6.7 billion in 2024 and is projected to reach $12.4 billion by 2033. Nuclear fusion materials represent a smaller but fast-growing niche driven by the need for plasma-facing components that can survive extreme conditions. This makes up a $165 billion opportunity altogether. 

The AI materials discovery field is evolving quickly, and competitors now fall into two clear camps: software platforms that use AI to accelerate R&D but do not operate physical labs, and automated labs that pair machine learning with robotics. Among its competitors, Radical's edge comes from its operational autonomous lab, which generates proprietary experimental data that compounds its models with every run. This closed-loop system, where each experiment refines the next prediction, is designed to be difficult to replicate and bound to grow more defensible over time. As Krause explains, they are testing tens of thousands of materials per week and are developing proprietary materials in hypersonics and nuclear fusion.

The autonomous lab where robotic synthesis meets a closed-loop AI system, each experiment accelerating the next

A core challenge across the sector to consider is the speed of adoption. While the demand undoubtedly exists, commercializing materials remains slow and resource-intensive. For Radical, this hurdle is especially notable since its defense, semiconductor, and fusion clients impose long qualification cycles before contracts convert to revenue. For instance, even if AI accelerates the R&D of these materials, actual customer adoption still requires integration into existing product architectures, validation of performance at the system level, and passing formal qualification (the process of certifying the material for real-world use), which collectively stretch into several years. Assuming the materials are validated and can effectively serve their purposes, the broader question then becomes whether AI-driven discovery can close the gap between insight and deployment fast enough to justify the capital now flowing into the field. Whoever can meet that demand will be a runaway winner, and Radical is positioning itself to be one of them.

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