Hexbyte Glen Cove A doubly magic discovery

Hexbyte Glen Cove

The deformed nucleus of zirconium-80 is lighter than the sum of the masses of its 40 protons and 40 neutrons. The missing mass is converted into binding energy through E=mc2. The binding energy is responsible for holding the nucleus together. Credit: Facility for Rare Isotope Beams

A team of researchers, including scientists from the National Superconducting Cyclotron Laboratory (NSCL) and the Facility for Rare Isotope Beams (FRIB) at Michigan State University (MSU), have solved the case of zirconium-80’s missing mass.

To be fair, they also broke the case. Experimentalists showed that zirconium-80—a zirconium atom with 40 protons and 40 neutrons in its core or —is lighter than expected, using NSCL’s unparalleled ability to create rare isotopes and analyze them. Then FRIB’s theorists were able to account for that missing piece using advanced and novel statistical methods.

“The interaction between nuclear theorists and experimentalists is like a coordinated dance,” said Alec Hamaker, a graduate research assistant at FRIB and first author of the study the team published 25 November in the journal Nature Physics. “Each take turns leading and following the other.”

“Sometimes theory makes predictions ahead of time, and other times experiments find things that weren’t expected,” said Ryan Ringle, FRIB Laboratory senior scientist, who was in the group that made the zirconium-80 measurement. Ringle is also an adjunct associate professor of physics at FRIB and MSU’s Department of Physics and Astronomy in the College of Natural Science.

“They push each other and that results in a better understanding of the nucleus, which basically makes up everything that we interact with,” he said.

So this story is bigger than one nucleus. In a way, it’s a preview of the power of FRIB, a nuclear science user facility supported by the Office of Nuclear Physics in the U.S. Department of Energy Office of Science.

When user operations begin next year, nuclear scientists from around the globe will have the chance to work with FRIB’s technology to create rare isotopes that would be impossible to study elsewhere. They’ll also have the opportunity to work with FRIB’s experts to understand the results of those studies and their implications. That knowledge has a range of applications, from helping scientists make more sense of the universe to improving cancer treatments.

“As we move forward into the FRIB era, we can do measurements like we’ve done here and so much more,” Ringle said. “We can push further beyond. There’s enough capability here to keep us learning for decades.”

That said, zirconium-80 is a really interesting nucleus in its own right.

For starters, it’s a tough nucleus to make, but making rare nuclei is NSCL’s specialty. The facility produced enough zirconium-80 to enable Ringle, Hamaker, and their colleagues to determine its mass with unprecedented precision. To do this, they used what’s known as a Penning trap mass spectrometer in NSCL’s Low-Energy-Beam and Ion Trap (LEBIT) Facility.

“People have measured this mass before, but never this precisely,” Hamaker said. “And that revealed some interesting physics.”

“When we make mass measurements at this precise a level, we’re actually measuring the amount of mass that’s missing,” Ringle said. “The mass of a nucleus isn’t just the sum of the mass of its protons and neutrons. There’s missing mass that manifests as energy holding the nucleus together.”

This is where one of science’s most famous equations helps explain things. In Albert Einstein’s E = mc2, the E stands for energy and m stands for mass (c is the symbol for the speed of light). This means that mass and energy are equivalent, although this only becomes noticeable in extreme conditions, such as those found at the core of an atom.

When a nucleus has more binding energy—meaning it’s got a tighter hold of its protons and neutrons—it’ll have more . That helps explain the zirconium-80 situation. Its nucleus is tightly bound, and this new measurement revealed that the binding was even stronger than expected.

This meant that FRIB’s theorists had to find an explanation and they could turn to predictions from decades ago to help provide an answer. For example, theorists suspected that the zirconium-80 nucleus could be magic.

Every so often, a particular nucleus bucks its mass expectations by having a special number of protons or neutrons. Physicists refer to these as magic numbers. Theory posited that zirconium-80 had a special number of protons and neutrons, making it doubly magic.

Earlier experiments have shown that zirconium-80 is shaped more like a rugby ball or American football than sphere. Theorists predicted that the shape could give rise to this double magicity. With the most precise measurement of zirconium-80’s mass to date, the scientists could support these ideas with solid data.

“Theorists had predicted that zirconium-80 was a deformed doubly-magic nucleus over 30 years ago,” Hamaker said. “It took some time for the experimentalists to learn the dance and provide evidence for the theorists. Now that the evidence is there, the theorists can work out the next few steps in the dance.”

So the dance continues and, to extend the metaphor, NSCL, FRIB, and MSU offer one of the finest ballrooms for it to play out. It boasts a one-of-a-kind facility, expert staff and the nation’s top-ranked nuclear physics graduate program.

“I am able to work onsite at a national user facility on topics at the forefront of nuclear science,” Hamaker said. “This experience has allowed me to develop relationships and learn from many of the lab’s staff and researchers. The project was successful because of their dedication to the science and the world-leading facilities and equipment at the lab.”

More information:
Alec Hamaker, Precision mass measurement of lightweight self-conjugate nucleus 80Zr, Nature Physics (2021). DOI: 10.1038/s41567-021-01395-w. www.nature.com/articles/s41567-021-01395-w

A doubly magic discovery (2021, November 25)

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Hexbyte Glen Cove New strategy puts evolution of microscopic structures on fast track thumbnail

Hexbyte Glen Cove New strategy puts evolution of microscopic structures on fast track

Hexbyte Glen Cove

Engineers at Rice University and Lawrence Livermore National Laboratory are using neural networks to accelerate the prediction of how microstructures of materials evolve. This example predicts snowflake-like dendritic crystal growth. Credit: Mesoscale Materials Science Group/Rice University

The microscopic structures and properties of materials are intimately linked, and customizing them is a challenge. Rice University engineers are determined to simplify the process through machine learning.

To that end, the Rice lab of materials scientist Ming Tang, in collaboration with physicist Fei Zhou at Lawrence Livermore National Laboratory, introduced a technique to predict the evolution of microstructures—structural features between 10 nanometers and 100 microns—in materials.

Their open-access paper in the Cell Press journal Patterns shows how (computer models that mimic the brain’s neurons) can train themselves to predict how a structure will grow under a certain environment, much like a snowflake forms from moisture in nature.

In fact, snowflake-like, dendritic crystal structures were one of the examples the lab used in its proof-of-concept study.

“In modern material science, it’s widely accepted that the microstructure often plays a critical role in controlling a material’s properties,” Tang said. “You not only want to control how the atoms are arranged on lattices, but also what the microstructure looks like, to give you good performance and even new functionality.

“The holy grail of designing materials is to be able to predict how a microstructure will change under given conditions, whether we heat it up or apply stress or some other type of stimulation,” he said.

Tang has worked to refine microstructure prediction for his entire career, but said the traditional equation-based approach faces significant challenges to allow scientists to keep up with the demand for new materials.

“The tremendous progress in machine learning encouraged Fei at Lawrence Livermore and us to see if we could apply it to materials,” he said.

Fortunately, there was plenty of data from the traditional method to help train the team’s neural networks, which view the early evolution of microstructures to predict the next step, and the next one, and so on.

“This is what machinery is good at, seeing the correlation in a very complex way that the human mind is not able to,” Tang said. “We take advantage of that.”

The researchers tested their neural networks on four distinct types of microstructure: plane-wave propagation, grain growth, spinodal decomposition and dendritic crystal growth.

In each test, the networks were fed between 1,000 and 2,000 sets of 20 successive images illustrating a material’s microstructure evolution as predicted by the equations. After learning the evolution rules from these data, the was then given from 1 to 10 images to predict the next 50 to 200 frames, and usually did so in seconds.

The new technique’s advantages quickly became clear: The neural networks, powered by graphic processors, sped the computations up to 718 times for grain growth, compared to the previous algorithm. When run on a standard central processor, they were still up to 87 times faster than the old method. The prediction of other types of evolution showed similar, though not as dramatic, speed increases.

Comparisons with images from the traditional simulation method proved the predictions were largely on the mark, Tang said. “Based on that, we see how we can update the parameters to make the prediction more and more accurate,” he said. “Then we can use these predictions to help design materials we have not seen before.

“Another benefit is that it’s able to make predictions even when we do not know everything about the material properties in a system,” Tang said. “We couldn’t do that with the equation-based method, which needs to know all the parameter values in the equations to perform simulations.”

Tang said the computation efficiency of neural networks could accelerate the development of novel materials. He expects that will be helpful in his lab’s ongoing design of more efficient batteries. “We’re thinking about novel three-dimensional structures that will help charge and discharge batteries much faster than what we have now,” Tang said. “This is an that is perfect for our new approach.”

More information:
Kaiqi Yang et al, Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks, Patterns (2021). DOI: 10.1016/j.patter.2021.100243

New strategy puts evolution of microscopic structures on fast track (2021, April 30)
retrieved 2 May 2021
from https://phys.org/news/2021-04-strategy-evolution-microscopic-fast-track.html

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