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 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.

Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning

“…A property graph database can be a powerful tool because connections, such as those representing the varied relationships between structures, properties, and processes in materials exploration, can be easily seen. 143 Visualization of graph databases is often used when there are large amounts of variables with many complex possible connections, as is the case in the different synthetic and processing conditions of silica aerogels and the relationship of those conditions to aerogel properties. To understand the influence of aerogel synthetic and processing variables, we developed the silica aerogel graph database (SiAGDB).…”

Extracting higher-conductivity designs for solid polymer electrolytes by quantum-inspired annealing

“…[1][2][3] Unlike human researchers, computers can obtain an overview of huge material structure databases and extract universal trends. New photoluminescent compounds, 4 solar cell components, 5 battery electrolytes, 6,7 and energy-related materials have been extracted through data science. 3,[8][9][10][11][12][13] In silico material screening is expected to accelerate research cycles and also is compatible with automated experiments.…”

Water-Assisted Ion Conduction in Solid-State Charge-Transfer Complex Electrolytes for Lithium Batteries

“…The concept of using charge-transfer (CT) polymer materials as battery electrolytes was first introduced in the patent literature . The three-part combination of polymer with electron-donating groups, small-molecule organic electron acceptors, and salt was reported to achieve surprisingly high ionic conductivity (10 –4 S/cm at room temperature). ,− Later, Oyaizu and co-workers reported that an array of small-molecule charge-transfer complexes mixed with lithium salt also exhibited elevated ionic conductivity (10 –6 to 10 –4 S/cm at room temperature) . The authors hypothesized that fast ion transport occurs at the interfaces between the charge-transfer crystals and salt.…”