Article on AU-led team joins AI and mater...

Auburn professor Reza Molaei won DOE funding for an AI-driven project on 3D-printed lattice structures, one of 278 selected from over 5,000 proposals. His team will use AI to predict fatigue in designs impossible to test physically.

Categorized in: AI News Science and Research
Published on: Aug 07, 2026
Article on AU-led team joins AI and mater...
Auburn University assistant professor Reza Molaei has received Department of Energy funding through the Genesis Mission, a White House-led initiative to apply AI to scientific discovery. His project on 3D-printed lattice structures was one of 278 selected from more than 5,000 proposals - fewer than 6% of submissions.

A federal push for AI-driven science

President Trump signed Executive Order 14363, "Launching the Genesis Project," in November. The order directs federal agencies to "create AI agents to test new hypotheses, automate research workflow and accelerate scientific breakthroughs" across manufacturing, biotechnology, critical materials, nuclear energy, and quantum information science. The project sits at the intersection of AI and materials science, a growing focus area within AI for Science & Research. Energy Secretary Chris Wright said the response to the mission shows the strength of America's research pipeline. "America has no shortage of bold ideas or talented scientists, and the response to the Genesis Mission proves that," he said.

Predicting fatigue in complex 3D structures

Molaei's Research focuses on triply periodic minimal surface (TPMS) structures - 3D geometries built from repeating internal surfaces. Engineers can design these structures to be lightweight, strong, and tunable for energy absorption, heat transfer, and fluid flow. The collaboration spans four institutions. Kansas State and Iowa State researchers handle AI. Oak Ridge National Laboratory manages additive manufacturing and materials selection. Molaei and Auburn colleague Robert Jackson lead mechanical testing and physics-based modeling. The core problem: there's no practical way to fatigue-test millions of structure variations. Molaei's team will use AI to predict fatigue life across design spaces that would be impossible to test experimentally. "We are not replacing physics with AI," Molaei said. "We are building on physics-based models and using AI to predict the conditions that we are not testing."

Medical and aerospace applications

TPMS structures have direct uses in medicine and aerospace. Solid bone implants can cause surrounding bone to weaken over time because the implant carries too much load. Porous TPMS implants can match the strength of a patient's bone, preventing that degradation. In aerospace, engineers want to reduce weight in UAVs and aircraft without sacrificing reliability. The porous design also improves heat exchangers, or radiators, allowing engines to transfer more heat outward. Molaei said the project could become one of the biggest accomplishments of his career and hopes Phase I will lay the groundwork for a larger Phase II.

Why this matters for science and research professionals

The project shows a practical model for combining physics-based modeling with AI prediction. Molaei's team uses AI to extend experimental reach, not replace it - an approach that could apply to other fields where physical testing is slow or expensive. The Genesis Mission's scale also signals where federal research funding is heading. With fewer than 6% of proposals funded, the selection is competitive, and Molaei's team is positioned to pursue Phase II.
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