Radical AI

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Radical AI

Radical AI

@RadicalAI

We discover and develop novel materials with artificial intelligence + automated labs.

New York City Katılım Aralık 2023
2 Takip Edilen3.7K Takipçiler
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Radical AI
Radical AI@RadicalAI·
History is defined by the materials we master. But the materials of the past cannot build the future. Radical AI is building scientific intelligence to autonomously discover and manufacture next-generation materials to solve the world's greatest challenges.
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Deep Tech Week
Deep Tech Week@deeptechweek·
The performance limits in every field of engineering are determined by material properties @RadicalAI led by @josephfkrause is developing a closed-loop robotic system with AI foundation models for material science To drive humanity farther, faster, more durably than ever
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Radical AI
Radical AI@RadicalAI·
What does it take to build at a speed that changes the world? The Brooklyn Navy Yard answered that once. Ships off the line in weeks. Intensity that answered to urgent needs. Radical AI is part of the reinvention of this address, bringing breakthrough materials to world-changing industries at a pace that’s never been possible before.
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Radical AI
Radical AI@RadicalAI·
Most AI in materials science produces predictions. Radical AI produces materials. This is how you cross the chasm between discovery and industry application.
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Radical AI
Radical AI@RadicalAI·
Turning raw elements into functional materials has always been slow. Teams of PhDs, years of work. We're building our second facility to do it all an order of magnitude faster, making and testing materials at a pace the field hasn't seen.
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Radical AI
Radical AI@RadicalAI·
The goal is simple: discovery that keeps pace with the questions worth asking. Gerbrand Ceder on why our expanded lab represents a fundamentally different relationship between scientists, their ideas, and our ability to build the future faster.
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Joseph Krause
Joseph Krause@josephfkrause·
The amount of materials challenges we face trying to reindustrializate America is endless. We @RadicalAI are working to start solving the most important.
SerenaB@RetVet99

What a time for U.S. manufacturers. Government is calling for innovative solutions to address some of America's most complex manufacturing and materials challenges relevant to the U.S. energy sector. Below is a list of top areas of interest. This isn't a surprise. The National Security Strategy, National Defense Strategy, and a slew of Executive Orders have signaled a surge in demand for these capabilities. For reindustrialization. To supercharge the defense industrial base. And now we're seeing the specific calls for innovation. One example: Department of Energy's HPC4EI initiative released a solicitation a few days ago. It's an opportunity for U.S. manufacturers to participate in collaborative projects with DOE national labs' supercomputing capabilities and technical expertise to strengthen domestic supply chains and enhance U.S. industrial competitiveness. Some of the solutions they are most interested in: - Overcoming qualification and certification barriers for advanced materials (time, cost) - Scaling and manufacturability / reducing cost and risk in scaling up advanced materials - Computational methods that reduce technical, cost, and supply chain risks for rare earth elements, battery materials, etc - Multifunctional materials w/ combined structural, thermal, and electrical performance - Advanced thermal management solutions for power electronics, semiconductors, computing systems - AI/ML driven materials informatics, end-to-end digital thread platforms, smart manufacturing approaches that enable faster development, reduced costs - Modeling of material systems that enable cost-effective domestic production -- e.g. rare-earth-free magnetic materials, substitutes for critical minerals - Advanced reactors, turbine systems, grid-scale storage - Simulation of advanced manufacturing methods Modeling of high-throughput machining, finishing, and forming technologies that deliver greater precision and reliability at scale - AI/ML and smart manufacturing platforms to optimize process control - Technologies that improve productivity optimization in energy-intensive industries - Chemicals and fuels -- process innovations, novel reactor and separation technologies, advanced materials development for chemical manufacturing - Iron and steelmaking, aluminum, and other metals -- innovative processes for primary metals production, methods to optimize productivity, methods to decopperize steel - Food and beverage products processing that optimize efficient recovery and reuse of waste energy, extend product shelf life, minimize waste - Cement and concrete, asphalt, and glass process innovations, alternative source materials, novel material composition, and solutions to reduce waste - Forest products, including novel dewatering or drying technologies and improved pulping and chemical recovery processes - Equipment and processes that improve industrial process heating, including reducing cost, improving efficiency, or enhancing product quality - Non-thermal processes for cost-competitive separations and treatments - Industrial technologies that can ensure grid reliability amidst industrial demand growth - Technologies that enable production of fit-for-purpose water -- e.g. reduce direct water consumption of data centers Concept papers due May 27.

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Radical AI
Radical AI@RadicalAI·
Packed rooms for Gerbrand Ceder and Stefano Falletta's talks at #S26MRS this week. The closed-loop materials design approach is capturing the imaginations of those who've spent their careers in the industry.
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Joseph Krause
Joseph Krause@josephfkrause·
AI data centers need massive power. But gas turbine supply is completely maxed out — order books full until 2029. The difference maker? Materials. Single-crystal turbine blades made from nickel superalloys. A materials engineering feat only 3 companies have perfected after 60 years. This is exactly the kind of hard materials problem @RadicalAI is built to solve. But we don’t just look backward, we are looking forward to what industry is next.
Gaurab Chakrabarti@Gaurab

You cannot buy a new gas turbine until 2030. Order books at GE, Siemens, and Mitsubishi stretch to 2029. Turbine prices have nearly tripled since 2019. Every AI data center needs power and every gas plant needs a turbine. And every turbine has one part that bottlenecks the entire industry: The blade. It has to survive in gas 500°C above the melting point of the metal it's made from and spin at up to 20,000 RPM under 10,000 g of centrifugal force. Each blade is grown as a single crystal of nickel superalloy, pulled through a vacuum furnace at 3 mm per minute. A set of blades costs $600,000 and takes 90 weeks to grow. The same metallurgy powers modern jet engines. Only 3 companies on Earth can build one. China spent $42 billion trying to catch up. They bought a Russian fighter engine, took it apart, and copied every part. Their copy ran 30 hours between overhauls versus 400 for the original. Modern Western engines run 4,000. You can reverse engineer the shape of a turbine blade. You cannot reverse engineer 60 years of metallurgy.

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Radical AI
Radical AI@RadicalAI·
How many breakthroughs are lost because we only track the result? At @DeepTechNY 2026, @josephfkrause introduced a visual language model designed to observe and understand science as it happens. Not just the result. The entire process. Because more visibility leads to more discovery.
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Radical AI
Radical AI@RadicalAI·
Gerbrand Ceder and Stefano Falletta are among the invited speakers joining @Materials_MRS #S26MRS this week. If you're on the ground in Honolulu, come see what we're working on at Radical AI.
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Radical AI
Radical AI@RadicalAI·
Turning experimental data into code makes new types of science possible.
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Radical AI
Radical AI@RadicalAI·
Automation without precision is not science. And science without automation misses millions of valuable data points that can fundamentally reshape how we make discoveries. @josephfkrause on what that looks like in our lab.
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Radical AI
Radical AI@RadicalAI·
Building an autonomous lab demands creativity. Precision dosing for pellets doesn’t exist in conventional systems. So we built it from scratch. An inside look at one small instance of the design thinking that enables the most advanced materials science lab in the world. A Radical AI build, captured by BAM.
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Radical AI
Radical AI@RadicalAI·
Before Radical AI: a scientist spends weeks on a single hypothesis. One idea, one experiment, one result at a time. After Radical AI: an AI agent generates millions of novel materials in a day, filters them down to the best candidates, and sends them directly to a lab to be built and tested. No waiting. No manual handoffs. @josephfkrause at @DeepTechNY 2026, on what it looks like when material science finally runs in parallel.
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Radical AI
Radical AI@RadicalAI·
Materials science simulations have been flying not just blind, but with incorrect navigation. There's a 13% disagreement rate between our benchmark and MPEA on phases and measured values. Prior benchmarks lack exact ground-truth annotations or documented methodology. We built LitXBench to change that.
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Jay Martel
Jay Martel@jaymos·
@RadicalAI @j_araujoneill that's fantastic, I was talking more about paying for your services As a smaller enterprise. but open sourcing parts of your workflow is also great. I couldn't find any Github repos or links on your website?
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Radical AI
Radical AI@RadicalAI·
$100M and 10 years to discover a new material? That’s the old model. Javier Araujo O’Neill (@j_araujoneill), VP of Finance at Radical AI, on how we deliver materials in weeks, removing the cost and time barriers that slow down progress. The future can’t wait.
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