Iwasaki Yuma, Ishida Masahiko, Shirane Masayuki
Central Research Laboratories, NEC Corporation, Tsukuba, Japan.
PRESTO, Japan Science and Technology Agency, Saitama, Japan.
Sci Technol Adv Mater. 2019 Dec 20;21(1):25-28. doi: 10.1080/14686996.2019.1707111. eCollection 2020.
High-throughput experiments (HTEs) have been powerful tools to obtain many materials data. However, HTEs often require expensive equipment. Although high-throughput ab-initio calculation (HTC) has the potential to make materials big data easier to collect, HTC does not represent the actual materials data obtained by HTEs in many cases. Here we propose using a combination of simple HTEs, HTC, and machine learning to predict material properties. We demonstrate that our method enables accurate and rapid prediction of the Kerr rotation mapping of an FeCoNi composition spread alloy. Our method has the potential to quickly predict the properties of many materials without a difficult and expensive HTE and thereby accelerate materials development.
高通量实验（HTEs）一直是获取大量材料数据的有力工具。然而，高通量实验通常需要昂贵的设备。尽管高通量从头计算（HTC）有潜力使材料大数据更易于收集，但在许多情况下，高通量从头计算并不能代表通过高通量实验获得的实际材料数据。在此，我们提出结合简单的高通量实验、高通量从头计算和机器学习来预测材料性能。我们证明，我们的方法能够准确、快速地预测FeCoNi成分渐变合金的克尔旋转映射。我们的方法有潜力在无需进行困难且昂贵的高通量实验的情况下快速预测许多材料的性能，从而加速材料开发。
