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High-throughput experiments facilitate materials innovation: A review

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Abstract

Since the Material Genome Initiative (MGI) was proposed, high-throughput based technology has been widely employed in various fields of materials science. As a theoretical guide, material informatics has been introduced based on machine learning and data mining and high-throughput computation has been employed for large scale search, narrowing down the scope of the experiment trials. High-throughput materials experiments including synthesis, processing, and characterization technologies have become valuable research tools to pin down the prediction experimentally, enabling the discovery-to-deployment of advances materials more efficiently at a fraction of cost. This review aims to summarize the recent advances of high-throughput materials experiments and introduce briefly the development of materials design based on material genome concept. By selecting representative and classic works in the past years, various high-throughput preparation methods are introduced for different types of material gradient libraries, including metallic, inorganic materials, and polymers. Furthermore, high-throughput characterization approaches are comprehensively discussed, including both their advantages and limitations. Specifically, we focus on high-throughput mass spectrometry to analyze its current status and challenges in the application of catalysts screening.

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Authors and Affiliations

  1. Materials Genome Institute, Shanghai University, Shanghai, 200444, China

    YiHao Liu, ZiHeng Hu, ZhiGuang Suo, LingYan Feng, XiuQing Gong, Yi Liu & JinCang Zhang

  2. Chongqing Key Laboratory of Green Synthesis and Application, College of Chemistry, Chongqing Normal University, Chongqing, 401331, China

    LianZhe Hu

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  1. YiHao Liu
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  2. ZiHeng Hu
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  3. ZhiGuang Suo
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  4. LianZhe Hu
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  5. LingYan Feng
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  6. XiuQing Gong
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  7. Yi Liu
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  8. JinCang Zhang
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Correspondence to LingYan Feng.

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Liu, Y., Hu, Z., Suo, Z. et al. High-throughput experiments facilitate materials innovation: A review. Sci. China Technol. Sci. 62, 521–545 (2019). https://doi.org/10.1007/s11431-018-9369-9

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