npj：薄膜材料库—高通量表征——计算——组合合成新材料

德国波鸿鲁尔大学机械工程学院材料研究所材料发现与界面研究首席Alfred Ludwig，提出使用薄膜材料库的组合合成和高通量表征，结合计算方法来发现新材料。此外，他认为薄膜材料数据库不仅需要对一种属性进行表征，还应针对尽可能多的功能属性进行全面的描述和开发。建立MLs是非常必要且实用的，如具有良好表征的材料数据库可用于外部研究小组的个性化研究，并进一步作（高通量）测定和分析。研究数据管理的实施对于实验组和计算组之间的合作至关重要。

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.