The report emerged from DOE's 2024 Basic Research Needs Workshop on Measurement Innovation, organized under the Office of Science's Fusion Energy Sciences (FES) program. It was led by Luis Delgado-Aparicio, head of advanced projects at Princeton Plasma Physics Laboratory (PPPL), and co-chaired by Sean Regan, director of the Experimental Division at the University of Rochester's Laboratory for Laser Energetics. Seventy researchers from academia, national laboratories, and private industry contributed findings across seven topic areas.
To operate a fusion system safely and at scale, engineers must continuously monitor plasma properties — temperature, density, and fuel conditions — that directly affect whether fusion reactions proceed, stall, or destabilize. Without reliable diagnostics, operators cannot make the real-time adjustments that commercial-grade reliability requires.
The report identifies four priority areas where current capabilities fall short. First, diagnostics capable of withstanding radiation levels expected inside commercial fusion power plants do not yet exist at production scale. Second, measurement techniques fast enough to capture the ultraquick processes in inertial confinement fusion (ICF) remain underdeveloped. Third, AI-assisted design tools that could accelerate the development of next-generation diagnostics are underutilized. Fourth, the pipeline of scientists entering diagnostics research lacks institutional support.
The report addresses diagnostics needs across the full spectrum of fusion and plasma research funded by FES:
The breadth of the review reflects DOE's intent for this work to support both the federal research program and a private fusion sector that now includes more than 40 active companies. Private investment in fusion has exceeded $7 billion since 2020, and commercial offtake agreements are already being signed for plants expected to come online in the early 2030s. That timeline creates urgency: diagnostic systems capable of supporting commercial operations need to be developed, tested, and qualified before those plants are built.
The report aligns explicitly with DOE's Fusion Science & Technology Roadmap, which targets the scientific and engineering foundation needed to support a competitive U.S. fusion industry through the mid-2030s. Its release comes as U.S. fusion advocates have raised concerns about China's accelerating investment and the risk that fragmented domestic funding could cede ground in a strategically significant energy technology.
"Measurement innovations have led and will continue to lead to scientific and engineering breakthroughs in plasma science," said Delgado-Aparicio. "This new report provides substantive findings across seven key areas. We believe it will impact both the public and private fusion communities in a meaningful way."
The report's findings on AI-assisted diagnostic design also intersect with a broader DOE and industry push to use machine learning to compress fusion development timelines — a theme that has gained traction as private fusion companies race to demonstrate net energy gain before capital commitments for first-of-kind commercial plants must be finalized.
