Material scientists at the University of Rochester are using machine learning to expedite the discovery of new crystalline materials with specific properties. By automating the classification of materials based on X-ray diffraction patterns using convolutional neural networks, this approach aims to accelerate materials innovation and benefit various technological applications, from electronics to sustainability.
ML boosts X-ray diffraction techniques to find new materials
Material scientists at the University of Rochester are using machine learning to accelerate the discovery of new materials. By automating the analysis of X-ray diffraction (XRD) patterns, researchers are able to process the overwhelming amount of data produced, facilitating the development of materials with specific properties.
Automating material analysis
The study led by materials science PhD student Jerardo Salgado developed deep learning models utilizing convolutional neural networks (CNNs) to automate the classification of materials based on their XRD patterns. These models were trained on a large dataset of synthetic XRD patterns, allowing for the rapid analysis of material properties, which can significantly expedite materials innovation and development for various technological applications.
Research from the Center for Matter at Atomic Pressures also indicates that using XRD combined with machine learning can not only assist in discovering new materials but also provide insights into the formation of stars and planets.
By utilizing AI to automate data analysis, scientists can focus their efforts on designing materials that will shape the future, driving innovation and scientific discovery.
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