TITLE:
A Proposal of Genuine Computing Methods in Materials Informatics
AUTHORS:
Raymond Wu, Susumu Otsuki
KEYWORDS:
Materials Informatics, Machine Learning, Bayesian, Data Foundation
JOURNAL NAME:
Journal of Computer and Communications,
Vol.13 No.12,
December
26,
2025
ABSTRACT: In the world of Material Informatics (MI), conventional methods involve tremendous laboratory work or extensive simulations that may not yield the expected results. Our objectives are to contribute to the originality of the standardization of the whole process of MI, starting with data quality measurement, the reusability of data and models, the development of a genuine data structure for machine learning, the alteration of design space, and finally, a novel coefficient-based analysis that can optimize the target yield and the likelihood. To achieve the objectives, our research has focused on the numerical analysis of informatics in materials science, supported by innovations in measurement and optimization processes. It also provides an overview of some of the recent successful data-driven MI strategies undertaken in this decade. The research also identifies some challenges the community is facing and those that should be overcome shortly, and streamlines a genuine process of MI.