Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database

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.

Automated summary

Data-driven material exploration is an important research method, but recording, analyzing, and sharing daily experimental results can be difficult. We have developed a data platform that accurately describes the relationships between structures, properties, and processes, and applied it to explore organic superionic glassy conductors through over 500 experiments. Automated data analysis has revealed the key factors for achieving remarkable room temperature ionic conductivity and Li+ transference number. Unlike previous research, all experimental results, including graphs, raw measurement data, and data processing systems, are accessible to everyone in a public repository. This direct data sharing will enhance scientific communication and accelerate the integration of material knowledge.