The quest to discover new crystal structures in materials has long been a cornerstone of scientific exploration, and has critical implications in diverse industries ranging from electronics to pharmaceuticals. Crystalline materials, defined by their ordered atomic arrangements, play an important role in technological advances. The precise identification and characterization of these structures has conventionally relied on methods such as X-ray powder diffraction. However, the emergence of multiphase samples with intricate mixtures of different crystal structures has posed challenges for accurate identification.
To address this challenge, a study by researchers at the Tokyo University of Sciences (TUS), Japan, in collaboration with renowned institutions, introduced a new deep learning model. The research describes 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 built a binary classifier using 80 convolutional neural networks. They trained this model using synthetic multiphase X-ray diffraction patterns designed to simulate anticipated i-QC phase patterns. After rigorous training, the model showed remarkable performance, with an accuracy of over 92%. It effectively detected an unknown i-QC phase within multiphase Al-Si-Ru alloys, confirming its proficiency in the analysis of 440 measured diffraction patterns of various unknown materials in six alloy systems.
Surprisingly, the model’s capability extended beyond the detection of predominant components, successfully identifying the elusive i-QC phase even when it was not the major component of the mixture. Furthermore, its potential extends beyond i-QC phases, suggesting applicability in the identification of novel decagonal and dodecagonal quasicrystals and various crystalline materials.
The model shows an accuracy that promises to accelerate the multiphase sample identification process. This advance, reinforced by the success of the model, is poised to revolutionize materials science by accelerating 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; introduces a paradigm shift in materials analysis. Its potential applications in various industrial sectors, from optimizing energy storage to advancing electronics, hold promise for transformative technological advances.
This research marks a notable step toward revealing new phases within quasicrystals, allowing scientists to navigate unexplored territories in materials science. The team’s pioneering work enriches our understanding of crystal structures and heralds a new era of accelerated discovery and innovation in materials science.
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Niharika is a Technical Consulting Intern at Marktechpost. She is a third-year student currently pursuing her B.tech degree at the Indian Institute of technology (IIT), Kharagpur. She is a very enthusiastic person with a keen interest in machine learning, data science and artificial intelligence and an avid reader of the latest developments in these fields.
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