Materials Database Group

Storage to knowledge, Database to Knowledge base

2022.05.10 Update

The Materials Database Group has been conducting research and development with the goal of creating a sustainable materials database and quality control even in the era of Big Data. As the world is flooded with a variety of data and information, the balance between quantity and quality in the collection of such data and information will make materials informatics more practical. On the other hand, not only the databases owned by NIMS, but also databases on various topics from various institutions can be linked on a single academic principle to create a large knowledge base and generate knowledge that is beyond the reach of human knowledge. The Materials Database Group aims to build a knowledge base that can be shared by materials researchers.

Areas of expertise and research targets

When we want to find something new, we try to put together the information we already know and use our wisdom. Whether we want to find a new material or find a way to make it, the first step is to collect as much information as possible that is already known in the world. Collecting data is not an easy task. In many cases, the best we can do is to gather narrow information on a limited number of topics. So, if we collaborate with other initiatives and share data, we can obtain a lot more information than we could do alone. However, there is a limit to the amount of big data that can be considered by humans, so we need a mechanism that allows machines to think based on some sort of algorithm. We will study the advanced utilization of such a database that integrates collection, collaboration, and consideration.

(1) Sustainable materials data collection and database creation

The creation of databases will be accelerated by the collaborative work of humans and artificial intelligence. We aim to create a sustainable database by balancing the quality by humans and the work efficiency by machines. We will promote the collection of data from publicly available information such as academic papers and patents, and create a database that will become a core in the research and development of materials.

(2) Collaboration of databases

It is not enough for a material to have high performance; it must also have a balance of various factors such as cost, safety, and environment before it can be put to practical use as a product. A system that can handle these various factors collectively is called linked data. Databases are linked together under a common machine-readable format based on international standards.

(3) Materials development through thinking and reasoning

Thinking and reasoning are the keys to extracting value from the big data created in (1) and (2). Based on problem solving methods such as deduction, induction, analogy, and hypothetical reasoning, we aim to explore unexplored areas in materials science. In particular, we aim to develop new methods of materials development using the fact that big data can be used to derive highly accurate answers even in analogy and hypothetical reasoning, where the conclusions are not always correct.

(4) Machine-readable compilation of scientific principles and construction of a knowledge base

Academic principles enable deeper thinking. We will attempt to compile the human knowledge that has been accumulated over a long history in a machine-readable format. Through a method of articulating concepts called ontology, we aim to bring to the surface deeper truths that are not immediately obvious and use them to develop materials.

Uncover, connect, use, and deepen data. To implement the technologies necessary for the utilization of data, with a special focus on materials development. This is the mission of the Materials Database Group.

  1. Hiroyuki Oka, Atsushi Yoshizawa, Hiroyuki Shindo, Yuji Matsumoto, Masashi Ishii, "Machine extraction of polymer data from tables using XML versions of scientific articles", SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS: METHODS, VOL. 1, 12-23, 2021.
  2. Sae Dieb, Kou Amano, Kosuke Tanabe, Daitetsu Sato, Masashi Ishii and Mikiko Tanifuji, “Creating Research Topic Map for NIMS SAMURAI Database Using Natural Language Processing Approach” SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS: METHODS, VOL. 1, 2-11, 2021.

Group Members

  • (Open in a new window)Yukari KATSURA
  • (Open in a new window)Hiroyuki OKA
  • (Open in a new window)Akira SUZUKI
  • We accomplish our mission with;
    Isao KUWAJIMA, Taichi ABE, Koji KIMOTO, Kazutaka MITSUISHI, Fumihiko UESUGI, Taichi IKEDA, Kazuto HIRATA, Kaname INAISHI and Miharu SHIMIZU

Inquiry about this page

Materials Database Group
1-2-1 Sengen, Tsukuba, Ibaraki,
305-0047 JAPAN
E-Mail: change "=" to "@")