"It's a dream come true," said ORNL neutron scattering scientist Hassina Bilheux. "It has been an honor and privilege to work with so many talented people dedicated to seeing VENUS through."
With its advanced features and the world's most intense pulsed neutron beams, VENUS is set to revolutionize research across multiple scientific disciplines. These include energy storage for improved batteries, materials science for more efficient construction materials, plant physiology for drought-resistant crops, and more. VENUS will drive scientific progress by offering high-resolution 3D imaging with enhanced contrast sensitive to atomic-scale structural details. Moreover, VENUS's use of neutrons ensures that even the most delicate materials studied at ORNL remain unaltered.
VENUS also integrates advanced computational methods to optimize neutron beam usage. During experiments, instrument scientists and users will employ AI to generate 3D models of samples from time-of-flight data with significantly fewer measurements. Instead of waiting days or weeks for 3D models from multiple radiographs, research teams will obtain their results by the time their experiments conclude.
"VENUS offers broad capabilities for access to unique contrasts from many fields in science," Bilheux stated. "And with the help of AI, researchers can get their 3D data as soon as the experiment is over."
VENUS further establishes the United States as a leader in the emerging field of neutron imaging.
"We are thrilled to offer such one-of-a-kind capabilities to user communities around the globe," said Jens Dilling, associate laboratory director for ORNL's Neutron Sciences Directorate. "Considering some of the challenges we face as a nation and around the world, science and the talented people who make it happen, as here at VENUS, might be our greatest hope."
The vision for VENUS began in 2006 when Bilheux proposed creating a neutron scattering instrument with enhanced imaging contrast for studying crystalline and amorphous samples at the atomic scale without causing damage. As an example, ORNL instrument scientists, in collaboration with NASA, used neutrons on the MARS beamline at ORNL's High Flux Isotope Reactor to study fragile moon rocks from the Apollo missions. The high-resolution virtual rendering produced by this effort will be complemented by future measurements at VENUS, aiding researchers in understanding the mineral content of the rocks for insights into early planetary formations and potential water sources on the moon.
No other research technique can nondestructively generate such detailed 3D information on atomic structures. Neutrons can pass through materials without causing damage, allowing neutron imaging scientists to probe thick samples, create 3D images, and build more complete microscale models of materials based on neutron scattering.
ORNL's AI-driven approach to neutron imaging began with the development of advanced algorithms that acquire data intelligently, autonomously, and rapidly. In collaboration with X-ray beamline teams at Brookhaven National Laboratory and Purdue University, ORNL's SNAP neutron imaging beamline team published their results in 'Scientific Reports', laying the groundwork for VENUS's AI capabilities.
"We're very thankful for collaborating with our X-ray colleagues on this AI project," Bilheux said. "This partnership helped set the stage for high-quality research results we expect will be game-changing in materials science."
The VENUS Advisory Committee, formed at the project's inception, has remained engaged throughout the development process. Physical construction of VENUS began in 2019, continued through the pandemic, entered its final stages in late 2023, and is nearing completion with user beamtime anticipated to start in the latter half of 2025.
"It takes a village to create such a complex instrument, and our entire ORNL team is very dedicated, which I'm very thankful for and very proud of," Bilheux commented. "Now she's ready - it's time to come try things and push the limits of the instrument."
Research Report:A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems
Related Links
VENUS at Oak Ridge
Understanding Time and Space
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