ADVANCED MATERIALS & PROCESSES | OCTOBER 2024 5 MACHINE LEARNING | AI MACHINE LEARNING PREDICTS POLYMER BEHAVIOR Researchers from the National Institute for Materials Science (NIMS), Japan, are using machine learning to predict the material properties of polymers. The team developed their new method on homo-polypropylenes, using x-ray diffraction patterns of the polymers under different preparation conditions to provide detailed information about their complex structure and features. Scientists then analyzed two datasets using Bayesian spectral deconvolution to extract patterns from the complex data. The first dataset was x-ray diffraction data from 15 types of homo-polypropylenes subjected to a range of temperatures, and the second was data from four types of homo-polypropylenes that underwent injection molding. The mechanical properties they analyzed included stiffness, elasticity, the temperature at which the material starts to deform, and how much it would stretch before breaking. The team found that the machine learning analysis accurately linked features in the x-ray diffraction imagery with specific materials properties of the polymers. Some of the mechanical properties were easier to predict from the x-ray diffraction data, while others, such as the stretching break point, were more challenging. The researchers believe their Bayesian spectral deconvolution approach could be applied to other data, such as x-ray photoelectron spectroscopy, and used to understand the properties of other inorganic and organic materials, too. www.nims.go.jp/eng. MACHINE LEARNING EXPLORES PHASE-CHANGE MATERIALS Cornell University researchers, Ithaca, N.Y., are using a combination of machine learning and x-rays to explore the unusual behavior of phase-change materials. Scientists have long known that the cubic phase of germanium telluride (GeTe) exhibits an unexpected rise in lattice thermal conductivity as its temperature increases, but they did not understand why. The researchers found that as a sample of GeTe is heated to the point where its phase changes from a rhombohedral structure to a cubic structure, the bonds between second-nearest neighbors Flow diagram of the procedure used to provide a highly accurate machine learning prediction model using only the XRD results of polymer materials. Courtesy of NIMS. of like atoms (Ge-Ge and Te-Te) strengthen considerably. Ge-Ge bond strength increased by 8.3% and the strength of Te-Te bonds increased by a remarkable 103% as the sample’s temperature rose from 693 to 850 K. The team used machine learning- assisted first-principles calculations corroborated by x-ray scattering measurements to computationally reproduce the increasing thermal conductivity trend for the first time. They then borrowed a chemistry technique to perform the bonding analysis and confirmed that these increasingly strong second-nearest neighbor bonds play a major role in GeTe’s previously unexplained increase in lattice thermal conductivity. Phase-change materials such as GeTe are valued for their usefulness in a range of optical and electronic applications. Their optical and electrical properties change significantly depending on which of several stable phase states they are in, and these phase states can be easily reversed. The research demonstrates an efficient pathway toward accurate modeling of materials near phase transitions or at high temperatures that have promise for phase change, thermoelectric, and other energy applications. cornell.edu. Overview of methodology for studying GeTe phase-change materials. Courtesy of Nature Communications. (a) (b)
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