Extreme temperature can do strange things to metals. In severe heat, iron ceases to be magnetic. In devastating cold, lead becomes a superconductor.
For the last 30 years, physicists have been stumped by what exactly happens to uranium ruthenium silicide (URu2Si2) at 17.5 kelvin (minus 256 degrees Celsius). By measuring heat capacity and other characteristics, they can tell it undergoes some type of phase transition, but that鈥檚 as much as anyone can say with certainty. Plenty of theories abound.
A Cornell collaboration led by physicist , the Dick & Dale Reis Johnson Assistant Professor in the 麻豆视频 and 麻豆视频, used a combination of ultrasound and machine learning to narrow the possible explanations for what happens to this quantum material when it enters this so-called 鈥渉idden order.鈥
Their paper, 鈥溾 published March 6 in Science Advances.
鈥淚n uranium ruthenium silicide, we have no idea what the electrons are doing in the hidden order state,鈥 said Ramshaw, the paper鈥檚 senior author. 鈥淲e know that they don鈥檛 become magnetic, we know that they don鈥檛 become superconducting, but what are they doing? There are a lot of possibilities 鈥 orbital order, charge density waves, valence transitions 鈥 but it鈥檚 hard to tell these different states of matter apart. So the electrons are 鈥榟iding,鈥 in that sense.鈥
Ramshaw and his doctoral student Sayak Ghosh used high-resolution ultrasound spectroscopy to examine the symmetry properties of a single crystal of URu2Si2 and how these properties change during the hidden order phase transition. Most phase transitions are accompanied by a change in symmetry properties. For example, solids have all their atoms lined up in an organized way, while liquids do not. These changes in symmetry aren鈥檛 always obvious, and can be difficult to detect experimentally.
鈥淏y looking at symmetry, we don鈥檛 have to know all the details about what the uranium is doing, or what the ruthenium is doing. We can just analyze how the symmetry of the system looks before the phase transition, and how it looks after,鈥 Ramshaw said. 鈥淎nd that lets us take that table of possibilities that theorists have come up with and say, 鈥榃ell, these are not consistent with the symmetry before and after the phase transition, but these are.鈥 That鈥檚 nice, because it鈥檚 rare that you can make such definitive yes and no statements.鈥
However, the researchers encountered a problem. To analyze the ultrasound data, they normally would model it with wave mechanics. But to study the purest form of URu2Si2, they had to use a smaller, cleaner sample. This 鈥渙ddly-shaped little hexagon chip,鈥 Ramshaw said, was too tiny and had too much uncertainty for a straightforward wave-mechanics solution.
So Ramshaw and Ghosh turned to , professor of physics and a co-author of the paper, and her doctoral student Michael Matty, to produce a machine-learning algorithm that could analyze the data and uncover underlying patterns.
鈥淢achine learning is not only for an image-like data or big data,鈥 Kim said. 鈥淚t can dramatically change the analysis of any data with complexity that evades manual modeling.鈥
鈥淚t鈥檚 hard, because the data is just a list of numbers. Without any sort of method, it has no structure, and it鈥檚 impossible to learn anything from it,鈥 said Matty, the paper鈥檚 co-lead author with Ghosh. 鈥淢achine learning is really good at learning functions. But you have to do the training correctly. The idea was, there is some function that maps this list of numbers to a class of theories. Given a set of numerically approximated data, we could do what is effectively regression to learn a function that interprets the data for us.鈥
The results from the machine-learning algorithm eliminated roughly half of the more than 20 likely explanations for the hidden order. It may not yet solve the URu2Si2 riddle, but it has created a new approach for tackling data analysis problems in experimental physics.
The team鈥檚 algorithm can be applied to other quantum materials and techniques, most notably nuclear magnetic resonance (NMR) spectroscopy, the fundamental process behind magnetic resonance imaging (MRI). Ramshaw also plans to use the new technique to tackle the unruly geometries of uranium telluride, a potential topological superconductor that could be a platform for quantum computing.
Contributing authors included researchers from National High Magnetic Field Laboratory, Los Alamos National Laboratory, Max Planck Institute for Chemical Physics of Solids in Germany and Leiden University in the Netherlands.
The research was supported by the U.S. Department of Energy, the National Science Foundation and the , with funding from the National Science Foundation鈥檚 Materials Research Science and Engineering Center program.
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