“Surprisingly, this approach was over 90% accurate when tested on more than 1500 known materials. This is an exciting result given the complexity of the conventional process.”Nina Andrejevic, MIT
Topological materials are an exotic class of materials whose surfaces exhibit different electrical or functional properties than their interiors. They have been a hot area of research since their experimental realization in 2007 – a finding that sparked further research and precipitated a Nobel Prize in Physics in 2016. These materials are thought to have great potential in a variety of fields, and might someday be used in ultraefficient electronic or optical devices, or as key components of quantum computers.
But there are many thousands of compounds that could theoretically have topological characteristics, and synthesizing and testing even one such material to determine its topological properties can take months of experiments and analysis. Now, a team of researchers from Massachusetts Institute of Technology (MIT), Harvard University, Princeton University and Argonne National Laboratory has come up with a new approach that can rapidly screen candidate materials and determine with more than 90% accuracy whether they are topological.
Using this new method, the researchers have produced a list candidate materials. A few of these were already known to have topological properties, but the rest are newly predicted by this approach. The researchers report their findings in a paper in Advanced Materials.
Topological materials are named after a branch of mathematics that describes shapes based on their invariant characteristics, which persist no matter how much an object is continuously stretched or squeezed out of its original shape. Topological materials, similarly, have properties that remain constant despite changes in their conditions, such as external perturbations or impurities.
There are several varieties of topological materials, including semiconductors, conductors and semimetals, among others. Initially, it was thought that there were only a handful of such materials, but recent theory and calculations have predicted that, in fact, thousands of different compounds may have at least some topological characteristics. The hard part is figuring out experimentally which compounds may be topological.
Applications for such materials span a wide range. They include devices that could perform the same kind of computational and data storage functions as silicon-based devices but with far less energy loss, or devices for harvesting electricity efficiently from waste heat, such as in thermal power plants or electronic devices. Topological materials can also have superconducting properties, which could potentially be used to build the quantum bits for topological quantum computers.
But all of this relies on developing or discovering the right materials. “To study a topological material, you first have to confirm whether the material is topological or not,” says Mingda Li, an assistant professor of nuclear science and engineering at MIT, “and that part is a hard problem to solve in the traditional way.”
A method called density functional theory is often used to perform initial calculations, which then need to be followed up with complex experiments that require cleaving a piece of the material to atomic-level flatness and probing it with instruments under high-vacuum conditions. “Most materials cannot even be measured due to various technical difficulties,” says MIT graduate student Nina Andrejevic. But for those that can, the process can take a long time. “It’s a really painstaking procedure,” she says.
Whereas the traditional approach relies on measuring the material’s photoemissions or tunneling electrons, the technique developed by Li and his team relies on absorption – specifically, the way the material absorbs X-rays. Unlike the expensive apparatus needed for the conventional tests, X-ray absorption spectrometers are readily available and can operate at room temperature and atmospheric pressure, with no vacuum needed. Such measurements are widely conducted in biology, chemistry, battery research and many other applications, but they had not previously been applied to identifying topological quantum materials.
X-ray absorption spectroscopy provides characteristic spectral data from a given sample of material. The next challenge is to interpret that data and how it relates to the topological properties. For that, the team turned to a machine-learning model, feeding in a collection of data on the X-ray absorption spectra of known topological and nontopological materials, and training the model to find the patterns that relate the two. And it did indeed find such patterns.
“Surprisingly, this approach was over 90% accurate when tested on more than 1500 known materials,” says Nina Andrejevic, adding that the predictions take only seconds. “This is an exciting result given the complexity of the conventional process.”
As with many results from machine learning, the researchers don’t yet know exactly why the model works or what the underlying mechanism is that links the X-ray absorption to the topological properties. “While the learned function relating X-ray spectra to topology is complex, the result may suggest that certain attributes the measurement is sensitive to, such as local atomic structures, are key topological indicators,” says Jovana Andrejevic, a graduate student at Harvard University and twin sister of Nina.
The team has already used the model to construct a periodic table that displays the model’s overall accuracy on compounds made from each of the elements. The model can serve as a tool to help researchers home in on families of compounds that may offer the right characteristics for a given application. The researchers have also produced a preliminary study of compounds that they have used this X-ray method on, without advance knowledge of their topological status, and compiled a list of 100 promising candidate materials – a few of which were already known to be topological.
This story is adapted from material from MIT, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.