TY - JOUR
T1 - Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database
AU - Hatakeyama-Sato, Kan
AU - Umeki, Momoka
AU - Adachi, Hiroki
AU - Kuwata, Naoaki
AU - Hasegawa, Gen
AU - Oyaizu, Kenichi
N1 - Funding Information:
This work was partially supported by Grants-in-Aid for Scientific Research (Nos. 21H04695, 21H02017, 20H05298, and 19H05814) from the Ministry of Education, Culture, Sports, Science and Technology, Japan. The work was partially supported by JST FOREST Program (Grant Number JPMJFR213V, Japan) and the Research Institute for Science and Engineering, Waseda University.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Data-driven material exploration is a ground-breaking research style; however, daily experimental results are difficult to record, analyze, and share. We report a data platform that losslessly describes the relationships of structures, properties, and processes as graphs in electronic laboratory notebooks. As a model project, organic superionic glassy conductors were explored by recording over 500 different experiments. Automated data analysis revealed the essential factors for a remarkable room temperature ionic conductivity of 10−4–10−3 S cm−1 and a Li+ transference number of around 0.8. In contrast to previous materials research, everyone can access all the experimental results, including graphs, raw measurement data, and data processing systems, at a public repository. Direct data sharing will improve scientific communication and accelerate integration of material knowledge.
AB - Data-driven material exploration is a ground-breaking research style; however, daily experimental results are difficult to record, analyze, and share. We report a data platform that losslessly describes the relationships of structures, properties, and processes as graphs in electronic laboratory notebooks. As a model project, organic superionic glassy conductors were explored by recording over 500 different experiments. Automated data analysis revealed the essential factors for a remarkable room temperature ionic conductivity of 10−4–10−3 S cm−1 and a Li+ transference number of around 0.8. In contrast to previous materials research, everyone can access all the experimental results, including graphs, raw measurement data, and data processing systems, at a public repository. Direct data sharing will improve scientific communication and accelerate integration of material knowledge.
UR - http://www.scopus.com/inward/record.url?scp=85136506575&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136506575&partnerID=8YFLogxK
U2 - 10.1038/s41524-022-00853-0
DO - 10.1038/s41524-022-00853-0
M3 - Article
AN - SCOPUS:85136506575
SN - 2057-3960
VL - 8
JO - npj Computational Materials
JF - npj Computational Materials
IS - 1
M1 - 170
ER -