Organic cathodes for lithium-ion batteries are one of the most promising and significant materials toward a sustainable society. The molecular design is a key to achieve superior performances beyond inorganic cathodes. The present work shows predictors of the reaction potential, specific capacity, and ideal energy density for organic cathodes. Straightforward prediction models of the performance were constructed by a combination of machine learning and chemical insight, namely, sparse modeling for small data (SpM-S), on a small data set as training data found in the literature. The prediction accuracy was validated using different literature data. The predictors can be applied to explore high-performance organic cathodes in a wide search space efficiently. Moreover, SpM-S afforded straightforward, interpretable, and generalizable prediction models compared to other machine-learning algorithms. The small-data-driven methodology can be applied for further exploration of materials, enhancement of performances, and optimization of processes.
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