Qi An

What they work on

1. 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.
2. Establish quantitative composition–property relationships that govern strength and ductility in five-metal high-entropy carbides, and release the AI platform as open-source software for other materials such as alloys and battery materials.
3. Test whether configurational entropy, the stabilizing effect of mixing many elements, can stabilize both strong complex concentrated alloys and lubricating spinel oxide surface films during sliding at high temperature.
4. Use density functional theory, molecular dynamics, phase-field modeling, additive manufacturing and high-temperature wear testing within a physics-guided machine-learning framework to design alloy compositions and processing conditions in reverse.
5. Create a large constrained density functional theory dataset for zinc sulfide under light, and use machine learning to turn it into force fields that capture how illumination changes the forces between atoms.
6. Simulate how light affects the motion of dislocations (line defects that carry deformation) in zinc sulfide with molecular dynamics and coarse-grained models, and derive mobility laws and flow rules that can explain photoplasticity.
7. Relate composition, atomic ordering and processing to spin-gapless behavior in Mn2CoAl thin films grown by sputter beam epitaxy, and nanopattern them into spin injection junctions for magneto-electric spin-orbit logic.

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Why it matters

This work could speed the discovery of ceramics and alloys that last in aircraft engines, power generation, nuclear energy and defense hardware. Such parts must survive extreme heat and wear that destroy today's materials. The open simulation tools and datasets could help design flexible electronics, deformable solar panels, photocatalysts and optical sensors. Its spintronic materials work aims to show how to make more efficient spin-based logic devices. These devices could be smaller, faster and lower-power than current chips.

Known grants

From NIH and NSF research grants. Labs often have other funding we can't see.

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The school

Iowa State University: 23 labs on PI Pages, $462M a year on research, 327 research doctorates a year.

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