A Classified Branch–CapNet: A Multi-Modal Model with Classified Branches for the Capacity Prediction of Li–Ion Battery Cathodes

Junghee Kim, Jaehyeok Yang and Daewon Chung ()
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Junghee Kim: Department of Advanced Battery Convergence Engineering, Dongguk University, Seoul 04620, Republic of Korea
Jaehyeok Yang: Department of Advanced Battery Convergence Engineering, Dongguk University, Seoul 04620, Republic of Korea
Daewon Chung: Department of Advanced Battery Convergence Engineering, Dongguk University, Seoul 04620, Republic of Korea

Mathematics, 2025, vol. 13, issue 22, 1-25

Abstract: Machine learning has emerged as a promising tool to accelerate the screening of lithium–ion battery electrode materials. Gravimetric capacity, a critical performance indicator governing electrode energy density, is intrinsically related to lithium insertion and extraction mechanisms, requiring sophisticated embedding approaches that capture the structural characteristics of cathode materials. The cathode material dataset from the Materials Project database comprises heterogeneous data modalities: numerical features representing chemical properties and categorical features encoding structural characteristics. Naive integration of these disparate data types may introduce semantic gaps from statistical distributional discrepancies, potentially degrading predictive performance and limiting model generalization. To address these limitations, this study proposes a Classified Branch–CapNet model that individually embeds four distinct types of categorical structural data into separate classified branches along with numerical data for independent learning, subsequently integrating them through a late fusion strategy. This approach minimizes interference between heterogeneous data modalities while capturing structure–property relationships with enhanced precision. The proposed model achieved superior performance with a mean absolute error of 2.441 mAh/g, demonstrating substantial improvements of 56.2%, 71.2%, 73.9%, and 51.1% over conventional deep neural networks, recurrent neural networks, long short-term memory architectures, and the encoder-only Transformer, respectively. Furthermore, it achieved the lowest root mean square error of 15.236 mAh/g and the highest coefficient of determination of 0.961, confirming its superior predictive accuracy and generalization capability compared with all benchmark models. Our model therefore demonstrates significant potential to accelerate the efficient screening and discovery of high-performance battery electrode materials.

Keywords: machine learning; lithium–ion batteries; gravimetric capacity prediction; multi-modal learning; material design (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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