It has long remained challenging to predict the properties of complex chemical systems, such as polymer-based materials and their composites. We have constructed the largest database of lithium-conducting solid polymer electrolytes (104 entries) and employed a transfer-learned graph neural network to accurately predict their conductivity (mean absolute error of less than 1 on a logarithmic scale). The bias-free prediction by the network helped us to find super­ionic conductors composed of charge-transfer complexes of aromatic polymers (ionic conductivity of around 10–3 S/cm at room temperature). The glassy design was contrary to the traditional concept of rubbery polymer electrolytes, but it was found to be appropriate to achieve fast, decoupled motion of ionic species from polymer chains and to enhance thermal and mechanical stability. The unbiased suggestions generated by machine learning models can help researches to discover unexpected chemical phenomena, which could also induce a paradigm shift of energy-related functional materials.

Contradictory to the conventional understanding of solid-state ionics, we find that some organic crystals are highly ion conducting (>10 −4 S/cm at room temperature). Through the microparticles of charge-transfer (CT) complexes, dissociated lithium ions move readily. Fast conduction is observed for a wide variety of compounds that form CT complexes, irrespective of the functional groups. Automatic relationship analysis via machine learning indicates the importance of polarization of the CT complexes for the ionic conduction. The decoupling system, where ion transport is not dominated by the segmental motion of media molecules, paves the way for achieving superionic properties in organic monomeric and polymeric conductors.

Automated molecule design by computers is an essential topic in materials informatics. Still, generating practical structures is not easy because of the difficulty in treating material stability, synthetic difficulty, mechanical properties, and other miscellaneous parameters, often leading to the generation of junk molecules. The problem is tackled by introducing supervised/unsupervised machine learning and quantum-inspired annealing. This autonomous molecular design system can help experimental researchers discover practical materials more efficiently. Like the human design process, new molecules are explored based on knowledge of existing compounds. A new solid-state polymer electrolyte for lithium-ion batteries is designed and synthesized, giving a promising room temperature conductivity of 10 −5 S cm −1 with reasonable thermal, chemical, and mechanical properties.

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.

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.