Brookhaven National Laboratory receives millions in federal grants for artificial intelligence research

Brookhaven Lab secured seven federal grants up to $750,000 each for AI in physics and microelectronics. The DOE funding could reach nearly $5 million.

Categorized in: AI News Science and Research
Published on: Jul 30, 2026
Brookhaven National Laboratory receives millions in federal grants for artificial intelligence research

Brookhaven National Laboratory has secured seven federal grants worth up to $750,000 each to integrate artificial intelligence into high-energy physics, microelectronics, and other scientific domains. The funding, part of the Department of Energy's Genesis Mission, underscores the growing role of AI in national research infrastructure.

The grants are among 278 awarded to academic, corporate, and government organizations participating in the Genesis Mission, a public-private partnership launched last year to advance the nation's scientific and energy capabilities. Brookhaven Lab will lead all seven projects, which range from stabilizing high-energy physics experiments to designing microelectronics and analyzing data from particle colliders. One project aims to develop AI that can generate its own scientific hypotheses.

"We are well positioned to continue really leading in AI, in science, and I think we've done a lot of the groundwork," said Hendrik Hamann, chief AI scientist for Innovation, Science, and Security at Brookhaven Lab. He spoke during the Genesis Mission's inaugural summit in Washington, D.C. last week.

The awards will fund researchers and resources over a roughly nine-month period. Hamann estimated the total funds could approach nearly $5 million after the lab negotiates final terms with the Energy Department. Brookhaven researchers will also contribute to 29 other projects led by partner organizations, including Stony Brook University, which separately received $1.6 million in Genesis Mission funding.

Project scope and scientific focus

The seven Brookhaven-led projects cover a wide range of complex topics. One will use AI and a cloud chamber to study cloud formation, heavy rainfall, and severe weather initiation. "To understand and forecast" events like Superstorm Sandy, "you need something like that" research, Hamann said. Other projects focus on using AI to stabilize high-energy physics experiments, design microelectronics, and analyze data from particle colliders.

The grants reflect a broader federal push to embed AI for Science & Research into national laboratories. Over $5 billion in federal funds have been committed to the national AI-science push, according to the White House.

Economic and regional impact

While the projects serve as incubators for future Genesis Mission work and are not directly aimed at commercial outcomes, Hamann said the research will affect Long Island's economy and residents. The cloud chamber project, for example, could improve severe weather forecasting for the region.

Local tech industry advocates see the funding as a signal of Long Island's growing reputation. "This could put Long Island on the map, having two powerhouses like Brookhaven National Lab and Stony Brook leading the charge on AI," said Peter Goldsmith, chairman of the Long Island Software & Technology Network. "It's a perfect partnership." Goldsmith added that the institutions' work in AI could attract smaller businesses and amplify the region's status as a tech hub. "They wouldn't come to us if they didn't think we had the talent," he said.

Why this matters for research scientists

The Genesis Mission awards highlight the Department of Energy's commitment to AI-driven discovery, creating new opportunities for scientists to work on projects that merge machine learning with physics, materials science, and climate research. The diversity of the funded projects signals strong demand for researchers who can apply AI techniques to experimental and theoretical problems. For scientists looking to build expertise, an AI learning path for research scientists offers structured skill development in the machine learning and data analysis methods these projects require.


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