Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods
By A. Ludwig. npj Computational Materials, 2019
301 citations recorded by the source corpus.
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.
Subfields: Catalysis, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/313dee59663e74a1ef6c70430339e92b9688386e
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In Catalysis
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Citations in the corpus
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- High-Entropy Alloys as Catalysts for the CO2 and CO Reduction Reactions (2019, unknown): “The adsorption energies of CO and H were calculated as214 ∆E∗CO = E∗CO − E∗ − ECO (1) ∆EH∗ = EH∗ − E∗ − 1 2 EH2 (2) where ∆E∗CO and ∆EH∗ are the adsorption energies of CO and H, E∗CO and EH∗ are the DFT215 energies of the relaxed slabs with the adsorbate, E∗ is the DFT energy of the slab without any”