A D V A N C E D M A T E R I A L S & P R O C E S S E S | J A N U A R Y / F E B R U A R Y 2 0 2 3 1 0 MACHINE LEARNING | AI AI AIDS MOLTEN SALTS RESEARCH In a new study, researchers at the DOE’s Argonne National Laboratory showed how artificial intelligence could help pinpoint the right types of molten salts, which can serve as both a coolant and fuel in nuclear power reactors that generate electricity without emitting greenhouse gases. They can also store large amounts of energy, which is increasingly needed on an electric grid with fluctuating sources such as wind and solar power. Scientists are exploring different combinations of salts to get the exact properties needed to cool and fuel a nuclear power reactor efficiently for decades. These properties include lower melting temperatures, the right consistency, and the ability to absorb high amounts of heat, among others. The potential variations are nearly endless. The study set out to determine whether computer simulations driven by machine learning could guide and refine real-world experiments at Argonne’s Advanced Photon Source (APS). Researchers used the powerful x-rays at the APS to better understand specific salt mixtures by looking closely at their structures. But the time and cost associated with real-world experiments makes it desirable to narrow the field of candidates that undergo inspection. Building on previous modeling that explored heat-resistant materials, researchers used active learning to create a transferable model to analyze molten salts. Rather than being fitted for one or two specific molten salt mixture compositions, the transferable model canbe applied tomixtures across the composition space. The model makes predictions based on principles rather than a set of predefined answers. Now that the researchers have shown this approach can work, the next step is to work with even more complex data. anl.gov. ADVANCED MATERIALS DESIGN WITH AI Microscopic materials analysis is essential to achieving desirable performance in next-generation nanoelectronic devices. However, the magnetic materials involved in such devices often exhibit incredibly complex inter- actions between nanostructures and magnetic domains, which makes functional design challenging. To address this, a team of researchers from Tokyo University of Science succeeded in automating the interpretation of microscopic image data. This was achieved using an “extended Landau free energy model” that the team developed using a combination of topology, data science, and free energy. The model could illustrate the physical mechanism as well as the critical location of the magnetic effect, anditproposedan optimal structure for a nano device. The model used physics- based features to draw energy land- scapes in the information space, which could be applied to understand the complex interactions at the nanoscale in a wide variety of materials. The team’s results indicate that the demagnetization energy near a defect gives rise to a magnetic effect, which is responsible for the “pinning phenomenon.” Further, the scientists could visualize the spatial concentration of energy barriers, a feat that had not been achieved until now. Finally, the team proposed a topologically inverse design of recording devices and nanostructures with low power consumption. The model proposed in this study is expected to contribute to a wide range of applications in the development of spintronic devices, quantum information technology, and Web 3. www.tus.ac.jp/en. Researchers are searching for the ideal characteristics of molten salt, which can serve as both a coolant and fuel in advanced nuclear reactors. An image depicting the extended Landau free energy model developed by a research team from Tokyo University of Science.
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