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

Kan Hatakeyama‐Sato, Momoka Umeki, Hiroki Adachi, Naoaki Kuwata, Gen Hasegawa, Kenichi Oyaizu

2022npj Computational Materials18 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Lossless compressionComputer scienceRaw dataGraphInformaticsMaterials informaticsElectrical conductorProcess (computing)DatabaseMaterials scienceTheoretical computer scienceArtificial intelligenceData compressionElectrical engineeringEngineeringNursingEngineering informaticsComposite materialHealth informaticsProgramming languageOperating systemMedicinePublic healthMachine Learning in Materials ScienceIonic liquids properties and applicationsConducting polymers and applications
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