Autonomous Laboratory Speeds Alloy Discovery in Brooklyn
The platform produced 300 new alloy compositions in 16 weeks, aiming to shorten discovery cycles by up to a decade.
Updated on Oct. 30, 2026 in Robotics
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Radical AI, a Brooklyn-based startup, is utilizing an autonomous laboratory platform to accelerate materials discovery. The facility at the Brooklyn Navy Yard produced 300 new alloy compositions in 16 weeks.
Why it matters
The platform aims to reduce supply chain dependence on foreign critical minerals by shortening materials development timelines. Radical AI seeks to lower development cycles to one to three years, contrasting with traditional timelines that can span over a decade.
The autonomous platform manufactures physical alloy samples weighing between 50 and 200 grams. This process utilizes integrated AI and robotics to design, create, and test materials, feeding test results back into machine-learning models to refine subsequent iterations.
The players
Radical AI
A Brooklyn-based company backed by RTX and Nvidia that operates an autonomous laboratory for materials discovery.
Sandia National Laboratories
A federal research and development center focused on national security technologies, including materials science.
The details
The laboratory platform operates as a closed-loop system where automated robotics handle the physical manufacturing of alloy samples. Once produced, these samples undergo testing, and the resulting performance data is ingested by machine-learning models—computational algorithms that identify patterns in data—to optimize the next round of experimental designs. By iterating through these cycles rapidly, the system automates the traditionally manual process of searching for new material properties.
Timeline
January 2024: Radical AI was founded.
September 2026: Radical AI partnered with Sandia National Laboratories.
The Tech Race
Radical AI is attempting to compress the 10 to 15 year development cycle typical of traditional materials discovery. By applying autonomous robotics to the research process, the company seeks to outpace legacy laboratory workflows.
Radical AI does not manufacture commercial quantities of alloys, so the platform primarily serves as a research engine for specialized industrial applications. Manufacturers and defense-related sectors are the primary entities affected as the platform targets reduced reliance on foreign mineral supply chains.
The takeaway
Radical AI demonstrates that automation can significantly increase the volume of material testing compared to traditional labor-intensive discovery. Readers should monitor the partnership with Sandia National Laboratories for evidence of successful deployment in complex metal-hydrogen environments.
Further reading
For broader trends in material science and automation, see our Robotics coverage.
Source note: This article includes information reported by MINING.
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