Researchers develop a deep learning model that can detect a previously unknown quasicrystalline phase present in multiphase crystalline samples
Crystalline materials are made up of atoms, ions, or molecules arranged in an ordered, three-dimensional structure. They are widely used for the development of semiconductors, pharmaceuticals, photovoltaics, and catalysts. The type of structures that fall into the category of crystalline materials continues to expand as scientists design novel materials to address emerging challenges pertaining to energy storage, carbon capture, and advanced electronics.
However, the development of such materials necessitates precise ways of identifying them. Currently, powder X-ray diffraction is widely used for this purpose. It identifies the structure of crystalline materials by examining scattered X-rays from a powdered sample. However, the task of identification becomes quite complex when dealing with multiphase samples containing different types of crystals with distinct structures, orientations, or compositions. In such cases, the accurate identification of the various phases present in the sample relies on the expertise of scientists, making the process time-consuming. To expedite this process, innovative data-driven methods, such as machine learning, have been used for distinguishing individual phases within multiphase samples. While substantial progress has been made in utilizing them for collecting information about known phases, the identification of unknown phases in multiphase samples still remains a challenge.
Now, however, researchers have proposed a new machine learning "binary classifier" model that can identify the presence of icosahedral quasicrystal (i-QC) phases―a kind of long-range ordered solids that have self-similarity in their diffraction patterns―from multiphase powder X-ray diffraction patterns. This study involved collaboration among Tokyo University of Science (TUS), National Defense Academy, National Institute for Materials Science, Tohoku University, and The Institute of Statistical Mathematics. It was led by Junior Associate Professor Tsunetomo Yamada from TUS, Japan, and was published in the Advanced Science journal on 14 November 2023.