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On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets.

ABSTRACT: Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and rapidly homing in on the underlying trend in multi-dimensional data. Here, we show that by employing an algorithm called the mean shift theory to a large amount of diffraction data in high-throughput experimentation, one can streamline the process of delineating the structural evolution across compositional variations mapped on combinatorial libraries with minimal computational cost. Data collected at a synchrotron beamline are analyzed on the fly, and by integrating experimental data with the inorganic crystal structure database (ICSD), we can substantially enhance the accuracy in classifying the structural phases across ternary phase spaces. We have used this approach to identify a novel magnetic phase with enhanced magnetic anisotropy which is a candidate for rare-earth free permanent magnet.

SUBMITTER: Kusne AG

PROVIDER: S-EPMC4163667 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Project description:The chemical footprint (ChF), which combines life cycle assessment (LCA) and quantitative risk assessment principles, shows promise for exploring localized toxicity impacts of manufacturing processes, which is not achievable with LCA alone. An updated ChF method was applied to the global annual production of a hard disk drive (HDD) rare-earth element (REE) magnet assembly, assuming a supply chain in East and Southeast Asia. Existing REE magnet assembly LCA inventories were combined with supplier manufacturing locations to create a cradle-to-gate spatial unit process inventory. Emissions from the electricity grid for each manufacturing site were downscaled to hydrobasins of interest using the Global Power Plant Database. The predicted no effect concentration (PNEC) was chosen as the ecotoxicity pollution boundary to determine the threshold for dilution of each chemical of concern (CoC) and to calculate the ChF. Finally, a high-resolution hydrological database provided volumes of the freshwater river reach draining each hydrobasin and was used to calculate the dilution capacity (DC), that is, the volume required to remain at or below the PNEC for each CoC. The total ChF of annual REE magnet assembly production was 6.91E12 m3 , with hotspots in watersheds in China and Thailand where REEs are processed and steel metalworking takes place. Metals were the primary CoCs, with cadmium and chromium(VI) comprising 77% of total ChF. Dilution factors ranged from 5E-09 to 9E + 03 of the DC of the waterbody, reflecting the spatial variability in both emissions and DC. An advanced ChF method was demonstrated for HDD REE magnets. Scoping is a key step required to reduce model complexity. The use of regionalized fate factors and standardized hydrological data sets improves the comparability of ChFs across hydrobasins. Additional work to combine data sets into readily available tools is needed to increase usability and standardization of the ChF method and promote wider adoption. Integr Environ Assess Manag 2023;19:272-283. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets.

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