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首页> 外文期刊>Npj Computational Materials >Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods
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Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods

机译:使用组合合成方法发现新材料,并结合计算方法对薄膜材料库进行高通量表征

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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.
机译:这种观点为实验人员提供了关于多元材料系统中材料发现的观点-从薄膜上的纳米颗粒到本体,基于组合的薄膜合成和高通量表征以及高通量计算和材料信息学。完整的多元材料通过薄膜材料库的组合综合,可以有效地制造出用于验证/伪造假设和预测所必需的所有成分的系统以及成分梯度,自动化的高质量高通量表征方法可以全面确定成分,结构和库中包含的材料的(多功能)特性,创建的多维数据集可进行数据驱动的材料发现,并通过组合处理来支持对新识别的材料进行有效的优化,此外,这些数据集是进行数据处理的基础多功能存在图,包括组成,加工,结构和特性之间的相关性,可用于未来材料的设计。

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