University at Buffalo has won three major federal grants to develop artificial intelligence systems that could dramatically speed up how scientists discover new materials and chemicals. The Department of Energy announced the awards as part of its Genesis Mission initiative, selecting UB's projects from a competitive pool of more than 5,000 submissions across the nation.

What Are These Three AI Projects Trying to Accomplish?

The three grants represent different frontiers in AI-driven materials science. The first project, called CLEAR-AI, focuses on creating an automated system to identify better catalysts for making carbon-based fuels and chemicals through electrosynthesis. The second explores using AI to control microbial communities that produce medium-chain carboxylic acids, which are used in aviation fuels and animal feed additives. The third is a collaborative effort with Stanford University to use AI for modeling how electrons behave in quantum-scale materials.

Each project tackles a fundamental challenge in materials science: the gap between discovery and application. Currently, scientists working on catalyst development perform many tasks separately, computational modeling, catalyst synthesis, materials characterization, and performance testing, which slows down the feedback loop for improving designs. CLEAR-AI aims to integrate all these activities into one continuous decision-making loop, allowing results from each experiment to guide the next most promising research direction.

"Our goal with CLEAR-AI is to help researchers identify promising catalyst and process conditions more quickly, reliably and resource-efficiently," said Jiayu Peng, assistant professor in the Department of Materials Design and Innovation at UB.

Jiayu Peng, Assistant Professor, Department of Materials Design and Innovation, University at Buffalo

How Can AI Speed Up Materials Discovery?

• Closed-Loop Automation: CLEAR-AI integrates physics-based modeling, automated experimentation, advanced materials characterization, electrochemical testing, and data-driven analysis into one continuous workflow, eliminating delays between separate research activities.
• Microbial Engineering: AI can predict and identify naturally occurring viruses called phages that selectively target unwanted bacteria in microbial communities, making it faster and less expensive than current trial-and-error methods to engineer microbes for producing valuable chemicals.
• Quantum Materials Modeling: AI combined with fundamental physics can model electron transport in materials only a few atoms wide, helping scientists identify new materials that maintain conductivity as electrical components shrink in modern technologies.

The microbial engineering project, led by Yinyin Ye, addresses a specific bottleneck in bioeconomy applications. When scientists try to use microbes to produce high-value chemicals, some microbes in the community help the process while others divert carbon toward unwanted byproducts, reducing overall efficiency. By using AI to identify phages that selectively kill the competing microbes, researchers could make the engineering process faster, cheaper, and more precise.

"The University at Buffalo is proud to contribute to the Genesis Mission's effort to accelerate discovery and strengthen America's future," said Venu Govindaraju, senior vice president for research, innovation and economic development at UB.

Venu Govindaraju, Senior Vice President for Research, Innovation and Economic Development, University at Buffalo

Why Does This Matter for American Competitiveness?

The Genesis Mission is a historic national initiative led by the Department of Energy designed to build the world's most powerful integrated science discovery platform. By combining AI, supercomputing, quantum systems, and advanced scientific instruments, the initiative aims to accelerate breakthroughs in energy, scientific discovery, and national security.

UB's three awards were among 278 projects selected from more than 5,000 submissions, reflecting the university's leadership in applied AI and quantum science. The funding, which UB anticipates will exceed $1 million with potential for additional support, demonstrates federal confidence in the university's ability to translate breakthrough discoveries into real-world solutions.

The third project, called ATOMIQ (AI-Driven Transport Optimization of Metallic and Interfacial Quantum Materials), addresses a critical problem in modern electronics. As electrical conductors shrink, imperfections in the channel interrupt the flow of electric current, generating heat and degrading device performance. By using AI to model electron behavior at the quantum scale, researchers aim to identify new materials that could mitigate these effects and keep devices functioning efficiently as they get smaller.

The CLEAR-AI project includes collaborators from the University of Pennsylvania, Virginia Tech, the University of Virginia, Brookhaven National Laboratory, and Oak Ridge National Laboratory, demonstrating how federal funding can coordinate expertise across institutions. If successful, the project could establish a scalable foundation for broader electrosynthetic applications, helping advance energy-efficient production of fuels and chemical building blocks while strengthening U.S. leadership in AI-enabled manufacturing.