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

Abstract

Data-driven material exploration is a ground-breaking research style; however, daily experimental results are difficult to record, analyze, and share. We report a new 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 and a lithium 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.

Progress in the Application of Mathematical Algorithms in Materials Science

“…In materials science, it is necessary to capture all relevant information to distinguish the environment of different atoms and crystals. [1] Mathematical algorithms are critical to machine learning. Machine learning is mainly divided into supervised learning, semi-supervised learning and unsupervised learning by learning method, of which supervised learning is the most widely used.…”