About Us
Extreme Performance
Materials engineered to perform above 1000°C, where most existing alloys fail.
Data-Driven Discovery
CALPHAD modeling and machine learning guide every step of alloy design.
Global Collaboration
Partnerships across 7 countries accelerate synthesis, testing, and validation.
Real-World Impact
From jet engines to rockets — enabling next-generation aerospace technology.
Our Team
The Challenge
Modern aerospace and energy systems demand materials that survive above 1000°C. Today's leading options all fall short in different ways:
Nickel Superalloys
Limited by melting points of 1250–1400°C, restricting performance in the most extreme environments.
Ceramic Matrix Composites
Excellent heat resistance, but high cost, difficult processing, and brittleness limit wider adoption.
Refractory Metals
Withstand extreme heat, but high density and poor oxidation resistance hold them back.
The RADISH Project
Traditional materials development is slow, costly, and limited to exploring only a tiny fraction of possible compositions. Yet the chemical design space for RCCAs contains billions of potential alloys. RADISH bridges this gap by combining computational modeling, combinatorial synthesis, high-throughput characterization, and machine learning into one powerful discovery pipeline.
The project follows an iterative, two-cycle approach: the first pass identifies promising RCCA compositions across the full compositional space, and the second refines synthesis and characterization to focus on the most promising candidates — continuously tightening the search with every round.
To discover RCCAs that combine high strength at 1000°C, ductility at room temperature, exceptional oxidation resistance, and reduced density — performance levels unattainable with current engineering materials.
Performance Targets
Concrete, measurable goals define success for every RCCA developed within RADISH:
Workflow
High-Throughput CALPHAD
The process begins with large-scale CALPHAD simulations that screen vast combinations of elements. This step identifies alloy compositions with desirable phase stability, melting temperatures, and density. It narrows billions of possible materials into a small set of high-potential candidates.
Combinatorial Synthesis
Selected compositions are synthesized as material libraries using physical vapor deposition. Each library contains gradual variations in composition, enabling hundreds of alloy variants to be produced in a single experiment. This dramatically increases experimental throughput while maintaining precise control over composition.
High-Throughput Characterization
Each material library is rapidly analyzed using XRF / EDS / ERDA to map chemistry, XRD to determine crystal structure and phases, and nanoindentation and micropillar tests. The result is a comprehensive dataset linking composition, structure, and mechanical performance across an entire library.
Machine Learning
Experimental data feed into machine-learning models trained to recognize structure–property relationships. They identify the most influential chemical and structural features, predict properties of unexplored compositions, and guide the next iteration of CALPHAD screening and synthesis — a feedback loop that accelerates discovery far beyond traditional trial-and-error methods.
Upscaling & Testing
The most promising alloys are produced in bulk using spark plasma sintering (SPS). They then undergo rigorous evaluation: high-temperature XRD, dilatometry, and calorimetry to confirm phase stability; compression and creep testing at elevated temperatures; and oxidation testing to assess environmental resistance. Only alloys that surpass state-of-the-art high-temperature materials progress to the final stage.
Next-Generation Alloys
A new class of high-strength, oxidation-resistant RCCAs capable of operating beyond the limits of today's structural materials — unlocking new possibilities for aerospace, energy, and extreme-environment technologies.