In a significant advancement for scientific research, Los Alamos National Laboratory scientists are harnessing artificial intelligence capabilities to optimize particle accelerators, potentially revolutionizing how these complex machines are operated and maintained.

How Los Alamos National Laboratory is Using AI to Improve Research:

The Laboratory’s Adaptive Machine Learning team, led by Alexander Scheinker, has developed novel machine learning algorithms that autonomously control and optimize particle accelerators in real-time. This technology represents a major step forward in the field of accelerator physics, where precise tuning is critical for experimental success.

“The results we’ve seen from our diffusion model studies show a great deal of promise,” Scheinker noted in a statement to Los Alamos National Laboratory. “Tuning accelerators with these kinds of advanced machine learning techniques will bolster our ability to drive scientific discovery.”

AI Revolution in Accelerator Science

Particle accelerators, which charge particles to high speeds for various research applications, have traditionally required extensive manual tuning by specialists. The new AI-driven approach could dramatically streamline this process, allowing for more efficient operation and potentially expanding scientific capabilities.

The work is part of a broader trend at Los Alamos, where researchers are increasingly leveraging artificial intelligence to tackle complex scientific challenges. The laboratory has positioned itself as a leader in public AI research, with projects spanning national security, scientific discovery, and threat reduction.

According to the Los Alamos Daily Post, these AI tools are specifically designed to improve the performance of accelerators—devices that are fundamental to numerous research initiatives at the laboratory. The technology could have wide-ranging implications for nuclear physics and other scientific disciplines that rely on accelerator technology.

Department of Energy Support

The U.S. Department of Energy has recognized the potential of this approach, providing funding through its Office of Science, Office of High Energy Physics Accelerator Stewardship program. Additional support comes from the Laboratory Directed Research and Development program at Los Alamos.

The DOE’s Office of Science Nuclear Physics program has invested approximately $16 million in “Artificial Intelligence and Machine Learning for Autonomous Optimization and Control of Accelerators and Detectors” awards. Los Alamos researchers are leading or participating in two of the 15 funded projects under this initiative.

These investments reflect growing recognition that AI can address numerous technical challenges in simulations, control, data acquisition, and analysis faced by complex scientific instrumentation like particle accelerators.

Scientific Publications and Ongoing Development

The Los Alamos team has published their findings in respected scientific journals. Two papers—”cDVAE: VAE-guided diffusion for particle accelerator beam 6D phase space projection diagnostics” and “Conditional guided generative diffusion for particle accelerator beam diagnostics”—were published in Scientific Reports, detailing their innovative approach.

Scheinker’s team is currently developing adaptive diffusion models specifically for the Laboratory’s LANSCE (Los Alamos Neutron Science Center) accelerator, demonstrating the practical application of their research to existing facilities.

This intersection of artificial intelligence and accelerator physics represents a convergence of two cutting-edge fields with potential to dramatically accelerate scientific discovery. By automating complex tuning processes that previously required expert human intervention, these AI algorithms could help maximize the scientific output of expensive accelerator facilities while potentially opening new frontiers in experimental physics.

As Los Alamos continues to develop and refine these AI techniques, the implications extend beyond accelerator physics to numerous scientific disciplines that depend on precisely controlled particle beams for their research.