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      High-Throughput Exploration of Evolutionary Structural Materials Translated title: Hochdurchsatzexploration evolutionärer Konstruktionswerkstoffe

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          While experimental high-throughput and computational methods exist for the development of functional materials, structural materials are still being developed on the base of experience, stepwise prediction and punctual support of computational models. As a result, many major breakthroughs have been and still are achieved by coincidence under non-intuitive conditions. Experimental high throughput methods allow to explore large process windows where no prediction is possible due to lack of existent data. This work proposes the high throughput method “Farbige Zustände” as a novel approach for the experimental exploration of structural materials. New methods for sample synthesis, treatment and characterization are developed as well as computational methods for ad-hoc data analysis, search and experiment planning.


          Während neue Funktionswerkstoffe heutzutage mit experimentellen Hochdurchsatz- und Berechnungsmethoden entwickelt werden, findet die Suche nach neuen Konstruktionswerkstoffen auch heute noch auf der Basis von Erfahrung, schrittweiser Prädiktion und lokal unterstützenden Berechnungsmodellen statt. Folglich waren und sind immer noch viele Durchbrüche in der Werkstoffentwicklung Zufallsentdeckungen unter nicht-intuitiven Bedingungen. Experimentelle Hochdurchsatzmethoden erlauben die Exploration weiter Prozessfenster, in denen aufgrund fehlenden Wissens noch keine Vorhersagen für eine schrittweise Vorgehensweise möglich sind. Diese Arbeit schlägt die neuartige Methode „Farbige Zustände“ für die experimentelle Hochdurchsatz-Exploration von Konstruktionswerkstoffen vor, die ein spezifisches Anforderungsprofil erfüllen. Neue Methoden für die Probensynthese, deren thermische und mechanische Behandlung sowie deren Charakterisierung werden ebenso entwickelt wie Methoden zur Ad-hoc-Datenanalyse, Suche und Versuchsplanung.

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          Most cited references 32

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          A high-throughput infrastructure for density functional theory calculations

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            Combinatorial search of thermoelastic shape-memory alloys with extremely small hysteresis width.

            Reversibility of structural phase transformations has profound technological implications in a wide range of applications from fatigue life in shape-memory alloys (SMAs) to magnetism in multiferroic oxides. The geometric nonlinear theory of martensite universally applicable to all structural transitions has been developed. It predicts the reversibility of the transitions as manifested in the hysteresis behaviour based solely on crystal symmetry and geometric compatibilities between phases. In this article, we report on the verification of the theory using the high-throughput approach. The thin-film composition-spread technique was devised to rapidly map the lattice parameters and the thermal hysteresis of ternary alloy systems. A clear relationship between the hysteresis and the middle eigenvalue of the transformation stretch tensor as predicted by the theory was observed for the first time. We have also identified a new composition region of titanium-rich SMAs with potential for improved control of SMA properties.
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              Is Open Access

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

              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.

                Author and article information

                HTM Journal of Heat Treatment and Materials
                Carl Hanser Verlag
                14 February 2018
                : 73
                : 1
                : 3-12
                1 University of Bremen, Faculty of Production Engineering, Badgasteiner Straße 1, 28359 Bremen, Germany, and Leibniz Institute for Materials Engineering IWT, Badgasteiner Straße 3, 28359 Bremen, Germany
                2 University of Bremen, Faculty of Production Engineering, Bremen, Germany, and Leibniz Institute for Materials Engineering IWT, Bremen, Germany
                Author notes
                3 ellendt@ (Corresponding author/Kontakt)
                © 2018, Carl Hanser Verlag, München
                Page count
                References: 37, Pages: 10
                Self URI (journal page):
                Fachbeiträge/Technical Contributions


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