Development of high-performance organic energy storage is one of the important challenges in recent materials science. Molecular design and synthesis have potential for enhancement of the performances. Efficient exploration and design of the molecules are required in a wide search space. In the present work, a capacity prediction model for organic anodes was constructed on small experimental data by sparse modeling, a method of machine learning, combined with our chemical insights. The straightforward linear regression model facilitated discovery of a high-performance active material for organic anodes in a limited number of experiments. A recommended compound, 5-formylsalicylic acid (SA-CHO), showed one of the highest performances in recent works, i.e., a specific capacity of 873 mA h g −1 at 100 mA g −1 (sample number: n = 3) with rate performance and cycle stability. The model can be applied to explore organic anode active materials with higher specific capacity.
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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