Tokyo University of Science Researchers Introduce Deep Learning Model to Detect Quasicrystalline Phase
The quest to uncover novel crystalline structures in materials has long been a critical part of scientific exploration in a variety of industries. Crystalline materials, characterized by their ordered atomic arrangements, play a crucial role in technological advancements. Identifying and characterizing these structures accurately has traditionally relied on methods like powder X-ray diffraction. However, the emergence of complex mixtures of different crystalline structures has posed challenges for precise identification.
Researchers from Tokyo University of Science (TUS), Japan, in collaboration with esteemed institutions, introduced a new deep learning model. The research outlines the development of a machine learning-based binary classifier capable of detecting an elusive icosahedral quasicrystal (i-QC) phase from multiphase powder X-ray diffraction patterns.
The researchers constructed a binary classifier using 80 convolutional neural networks, training it using synthetic multiphase X-ray diffraction patterns. After rigorous training, the model exhibited remarkable performance, boasting an accuracy exceeding 92%. It effectively detected an unknown i-QC phase within multiphase Al-Si-Ru alloys, confirming its prowess in analyzing diffraction patterns from diverse unknown materials across six alloy systems.
The model’s capability extended beyond detecting predominant components, successfully identifying the elusive i-QC phase even when it wasn’t the primary constituent in the mixture. Additionally, its potential spans beyond i-QC phases, hinting at applicability in identifying new decagonal and dodecagonal quasicrystals and various crystalline materials.
The model showcases an accuracy that promises to expedite the identification process of multiphase samples. This breakthrough, bolstered by the model’s success, is poised to revolutionize materials science by expediting phase identification, which is crucial in mesoporous silica, minerals, alloys, and liquid crystals.
The impact of this study transcends the mere identification of quasicrystalline phases; it introduces a paradigm shift in material analysis. Its potential applications in diverse industrial sectors hold promise for transformative technological advancements.
This research signifies a remarkable stride toward unveiling new phases within quasicrystals, empowering scientists to navigate uncharted territories in material science. The team’s pioneering work enriches our understanding of crystalline structures and heralds a new era of accelerated discovery and innovation in materials science.
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