Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning

Yuma Iwasaki; Masahiko Ishida; Masayuki Shirane

doi:10.1080/14686996.2019.1707111

Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning

Sci Technol Adv Mater. 2019 Dec 20;21(1):25-28. doi: 10.1080/14686996.2019.1707111. eCollection 2020.

Authors

Yuma Iwasaki^{1

2}, Masahiko Ishida¹, Masayuki Shirane^{1

3}

Affiliations

¹ Central Research Laboratories, NEC Corporation, Tsukuba, Japan.
² PRESTO, Japan Science and Technology Agency, Saitama, Japan.
³ Nanoelectronics Research Institute (NeRI), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.

Abstract

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 Fe_xCo_yNi_1-x-y 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.

Keywords: 404 Materials informatics / Genomics; Materials informatics; ab-initio; combinatorial; high-throughput; machine learning.

Grants and funding

This work was supported by JST-PRESTO ‘Advanced Materials Informatics through Comprehensive Integration among Theoretical, Experimental, Computational and Data-Centric Sciences’ [Grant JPMJPR17N4].