Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods
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Abstract
Abstract This perspective provides an experimentalist’s view on materials discovery in multinary materials systems—from nanoparticles over thin films to bulk—based on combinatorial thin-film synthesis and high-throughput characterization in connection with high-throughput calculations and materials informatics. Complete multinary materials systems as well as composition gradients which cover all materials compositions necessary for verification/falsification of hypotheses and predictions are efficiently fabricated by combinatorial synthesis of thin-film materials libraries. Automated high-quality high-throughput characterization methods enable comprehensive determination of compositional, structural and (multi)functional properties of the materials contained in the libraries. The created multidimensional datasets enable data-driven materials discoveries and support efficient optimization of newly identified materials, using combinatorial processing. Furthermore, these datasets are the basis for multifunctional existence diagrams, comprising correlations between composition, processing, structure and properties, which can be used for the design of future materials.
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Research context
OpenAlex topics: Machine Learning in Materials Science · Catalysis and Oxidation Reactions · Catalytic Processes in Materials Science
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (60)
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