Izabela Szlufarska
University of Wisconsin-Madison, WI·Materials Science
Jet engines, nuclear reactors, hypersonic vehicles and advanced manufacturing all need materials that survive conditions that destroy ordinary metals: very high temperatures combined with mechanical stress, corrosive chemistry and radiation. Existing materials force tradeoffs between strength, durability, how easily they can be manufactured, and cost.
- Fields
Materials Science
Engineering
Computer Science
- Topics
extreme environments
high-temperature materials
refractory metals
- Grants
- 1 known grant, through Aug 2032
What they work on
- Determine how chemical and structural complexity across multiple length scales controls the processing, microstructural evolution and performance of high-temperature structural materials, including refractory metals, ceramics and composites.
- Build a closed-loop discovery framework that links high-throughput synthesis, additive manufacturing with real-time process monitoring, spark plasma sintering and extreme-environment testing with AI-guided materials design.
- Observe materials as they change during synthesis, heat treatment, oxidation and corrosion using in-situ diagnostics and synchrotron X-ray beamlines at the Advanced Photon Source.
- Develop computational capability spanning thermodynamic modeling, machine-learning interatomic potentials, mesoscale simulation, active learning and uncertainty-aware machine learning to predict behavior in extreme environments.
- Support an open national user community through staff-assisted experimentation, training workshops, hackathons and summer schools, and FAIR practices that make platform data and models findable and reusable.
More about the research
Finding better ones is slow because synthesis, testing and modeling usually happen in separate labs on separate timescales.
Why it matters
Energy production, transportation, national security and advanced manufacturing all depend on materials that hold up in extreme heat, stress, corrosion and radiation, and progress is limited by how slowly new ones are found and qualified. By offering open access to synthesis, extreme-environment testing, synchrotron characterization and AI-guided design in one place, along with shared FAIR data and trained users, the platform aims to expand national capacity for materials innovation and shorten the path from discovery to deployable technology.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Jaime Marian
University of California-Los Angeles, CA·Materials Science
Determine how 'necklace' microstructures nucleate at grain boundaries during dynamic recrystallization in body-centered-cubic refractory multi-principal element alloys, using mesoscale modeling.
Jun Li
Embry-Riddle Aeronautical University-Daytona Beach, FL·Engineering
Develop direct mesoscale simulations of 3D printing that couple heat, mechanical forces, chemistry and fluid-structure interactions, to show how temperature gradients and melting between filaments and reinforcing fibers lead to micro-cracks.
Qi An
Iowa State University, IA·Materials Science
Build a closed-loop AI platform that compares Bayesian optimization (choosing the most informative next test) with deep reinforcement learning (trial-and-reward learning) for designing the high-entropy carbide (Ti-Zr-Hf-Nb-Ta)C, using machine-learning force-field atomistic simulations.
The school
University of Wisconsin-Madison: 91 labs on PI Pages, $1.9B a year on research, 854 research doctorates a year.
Reach out
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