=Paper=
{{Paper
|id=Vol-3036/keynote01
|storemode=property
|title=Construction of Integrated Materials Data System for Data-driven Materials Research (keynote abstract)
|pdfUrl=https://ceur-ws.org/Vol-3036/keynote01.pdf
|volume=Vol-3036
|authors=Yibin Xu
|dblpUrl=https://dblp.org/rec/conf/rcdl/Xu21
}}
==Construction of Integrated Materials Data System for Data-driven Materials Research (keynote abstract)==
<pdf width="1500px">https://ceur-ws.org/Vol-3036/keynote01.pdf</pdf>
<pre>
   Construction of Integrated Materials Data System for
             Data-driven Materials Research

                                        Yibin Xu

         National Institute for Materials Research, Tsukuba Ibaraki 3050047, Japan
                                 Xu.yibin@nims.go.jp

Big data based artificial intelligence has been greatly expected to change the style of
materials research, and improve the efficiency and decrease the cost of materials devel-
opment. However, in spite of the great efforts done on data collection and database
construction since 1880’s, data shortage is still the bottleneck of today’s data-driven
materials research.
    Data integration has been addressed as one of the key issues of materials data system
for decades. In recent years, some data formats and protocols have been proposed for
data exchange between different data resources. Nevertheless, material identification is
still a confusing problem, since lack of common descriptors for all materials fields. In
this presented work, based on the statistics of NIMS Inorganic Materials Database
AtomWork-Adv, we proposed a set of descriptors to define a substance, and created a
substance dictionary containing more than 158,000 substances. With this dictionary,
users can identify their own materials at substance level, and make links to the crystal
structure and property data in AtomWork-Adv. We also show an example of data struc-
ture for multiphase and composite materials and how to link them with the substance
data.
    Addressing the data shortage problem, we analyzed the distribution of available
property data versus substance. This helps us to understand the current situation of data
availability and set up plans of data generation. We also show that a key to small data
issue is to leverage the correlations between properties. Since properties dominated by
same physical and chemical factors tend to have strong correlationship, it gives us a
chance to use the property with sufficient data as a substitute of that with a few data
available. Several examples of our data-driven researches with small data set are intro-
duced.




Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).



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