NEW ORLEANS — Tulane University is among the research teams selected by the U.S. Department of Energy to receive Phase I funding through the Genesis Mission, a national AI initiative aimed at advancing energy technologies, scientific discovery and national security. The announcement was made July 22 at the Genesis Mission Summit in Washington, D.C.
Tulane’s interdisciplinary research team will combine AI, quantum-mechanical simulations and laboratory experiments to identify new superconductors and quantum materials that could one day improve the electrical grid, medical imaging, quantum computing and other advanced technologies.
During the approximately nine-month Phase I period, participating teams will develop and test their AI-driven research frameworks. The Department of Energy will then select a smaller group of projects for multi-year Phase II awards ranging from $6 million to $15 million.
AI to Accelerate Materials Discovery
The Tulane team aims to accelerate the discovery of new superconductors and quantum materials by combining AI, high-fidelity quantum-mechanical simulations and laboratory experiments. The goal is to identify materials that could make everything from data centers and the nation’s electrical grid to MRI machines and quantum computers faster, more efficient and less energy-intensive.
Researchers say identifying new superconducting materials has traditionally required years of laboratory experiments because of the nearly limitless number of possible material compositions and atomic structures.
“Discovering a new quantum material is extraordinarily difficult because the number of possible compositions and atomic structures is almost limitless,” said Jianwei Sun, the project’s principal investigator and a professor of physics and engineering physics at Tulane University School of Science and Engineering. “By combining high-fidelity quantum-mechanical calculations, physics-aware AI and experimental measurements, we want to create a scientific workflow that learns continuously and directs researchers toward the most promising possibilities.”
Unlike generative AI systems trained on language, the Tulane team’s AI will be built on scientific data from quantum-mechanical calculations and experimental measurements, with the laws of physics incorporated directly into the model.
“Our AI is not a chatbot,” said Aron Culotta, a Tulane professor whose work focuses on AI and machine learning. “Instead of learning patterns in language, it will learn patterns in atoms, electrons, magnetism and atomic vibrations that can help predict how a material will behave.”
While superconductors can carry electrical current with no resistance, they must be cooled to extremely low temperatures, making them costly and difficult to use outside specialized settings. A superconductor that operates at substantially higher temperatures could contribute to a more efficient and resilient electrical grid, improved medical imaging systems, more powerful magnets, energy-efficient data centers, advanced transportation technologies, quantum computing and fusion energy.
Tulane Cross-Disciplinary Research
The team’s computational predictions will be tested through laboratory experiments. Daniel B. Straus, an assistant professor of chemistry at Tulane School of Science and Engineering, will help translate the team’s predictions into materials that can be synthesized and studied at Tulane.
The researchers are also collaborating with Huibo Cao of Oak Ridge National Laboratory, where neutron-scattering experiments will help determine the crystal and magnetic structures of selected materials and test the team’s predictions.
The project combines expertise across multiple scientific disciplines. Theoretical and computational physicists will develop the mathematical models and quantum-mechanical simulations that predict how materials should behave, while AI and data scientists will analyze the results to identify the most promising candidates for further study. Materials chemists will synthesize the most promising candidates, high-performance computing specialists will provide the computing power needed to run the simulations, and experimental researchers will verify whether the materials perform as predicted.
“What makes this project especially exciting is its interdisciplinary team,” Sun said. “Physicists, chemists, AI researchers and experimental scientists are working together on a problem that none of us could solve alone.”
“There is still no general theory of high-temperature superconductivity,” said Ruiqi Zhang, a research assistant professor in Tulane University’s Department of Physics and Engineering Physics, who helped develop the project’s computational framework. “Combining high-fidelity quantum-mechanical simulations, experimental data and physics-informed AI could provide a new pathway for discovering superconductors and quantum materials.”
