Many of us are familiar with the idea of tricorders, the fantastic fictional devices first seen on the original Star Trek television show that seemed to provide instant information on anything from the health of a crew member to the atmosphere of a planet. In typical science fiction fashion, Captain Kirk and his science officers could learn everything they needed to know about the rocks and air on alien planets in mere seconds with a quick scan.
Along with the love of science fiction often comes the desire to make the amazing future devices into real-life science fact, and a team of researchers at The University of Tokyo have taken a step towards making the tricorder concept a reality.
The Tokyo researchers have developed a powerful machine learning algorithm capable of predicting the properties and structures of unknown samples from an electron spectrum. This process may rapidly accelerate the process of discovering and testing novel nanomachines, solar cells, and other electronic devices that rely on unique materials.
“With increasing demand for nanoscale devices, in which peculiar atomic arrangements influence material properties more than those in bulk, the importance of understanding the local atomic structure is rapidly increasing,” states the introduction to the team’s paper. “Therefore, characterizing atomic and electronic structures on local scale, i.e. determining atomic structures and revealing their elements and chemical bonding, is indispensable in modern nanomaterials research.”
They used data from core-loss electron spectroscopy, a set of standard laboratory tests that send electrons at a sample to determine the atomic elements in it and their bonding structure. However, the results from these instruments are difficult to interpret. To overcome this problem, the research team turned to machine learning.
In contrast to conventional computer programs, machine learning algorithms do not need to be told what patterns to look for. Instead, the algorithms are trained by inputting many examples, and over time the program learns how to classify new unknown samples.
Here, the researchers chose a neural network that mimics the organization of the human brain. Data from known materials are sent as input, and the connections between neurons are adjusted to optimize the model's predictions.
According to the paper’s first author Shin Kiyohara, "with the increasing demand for nanoscale devices, tools for understanding molecular structures are becoming more and more valuable."
Although still a long way from a tricorder that can instantly identify alien rock formations, lead author Teruyasu Mizoguchi believes that "this method has enormous potential for use in quickly testing the properties of new materials."
To learn about other efforts to engineer futuristic tricorder-like devices, check out these stories: