Ciencias Exactas y Ciencias de la Salud

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A robust and interpretable machine learning framework for vanadium oxide supercapacitors
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06-13) Ortiz Aldana, Emmanuel Alexei; Kumar, Rudra; emimmayorquin; Mallar, Ray; Sánchez Ante, Gildardo; Kumar, Kishant; School of Engineering and Sciences; Campus Monterrey; Ebrahimibagha, Dariush
As global energy demands intensify, the development of efficient, scalable and reliable energy storage systems becomes increasingly urgent. While lithium-ion batteries dominate the current market, their low power density makes them unsuitable for current fluctuations degrading their life expectancy. Supercapacitors (SCs) with pseudocapacitance materials such as vanadium oxide offer an attractive option, with high power density, long life cycle and fast charge-discharge rate. However, their low energy density remains a major bottleneck limiting broader adoption. Current supercapacitor research is focused on improving the specific capacitance and thus expanding their energy density, nevertheless this is mostly done on traditional trial and error experiments, making it time-consuming, slow and expensive. Materials Informatics offers a paradigm shift by implementing machine learning (ML) techniques to uncover patterns in existing data and accelerate the design of novel materials. Despite promising results, many current materials ML studies suffer from limitations such as small data range, improper data preprocessing, target leakage, and lack of reproducibility due to unshared code and datasets. In this work a robust machine learning framework was developed for vanadium oxide SCs, designed to extract interpretable insights from manually gathered literature data. A rigorous cross-validation (CV) pipeline was implemented to ensure reliable model evaluation, avoiding common pitfalls such as overfitting and data leakage. Among the evaluated models, a Voting Regressor combining Ridge Regression, Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) achieved the best performance with a mean absolute error (MAE), root mean squared error (RMSE), and 𝑅2 of 81 𝐹 𝑔 ⁄ , 104 𝐹 𝑔 ⁄ and 0.61, respectively. To extract insights from the models, interpretability algorithms, including permutation importance (PI) and SHapley Additive exPlanations (SHAP) values were employed. Binder-free electrodes, wider potential windows, and a low current density are consistently associated with higher specific capacitance predictions. These findings highlight the potential of interpretable methods to uncover the ML models behavior and lead guided design of SCs.
Recent advances and perspectives on nanostructured carbon materials for supercapacitors
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-30) Higuera Martinez, Jose Ramiro; Pérez González, Víctor Hugo; 349700; Rodríguez Macías, Fernando Jaime; emipsanchez; Pérez González, Víctor Hugo; Escuela de Ingeniería y Ciencias; Campus Monterrey; Martínez Chapa, Sergio Omar
Supercapacitors are an important part of the electrical components market, as both current dampener, and as energy storage devices. Making carbon based supercapacitors is a still growing area of research, as carbon devices have shown to produce a wide range of results by modifying the chemical composition and nanostructure of carbon, as well as doping, and using different allotropes. This can, in principle, allow the improvement of experimental parameters desired from carbon supercapacitors, such as high power and energy density, as well as long-lasting devices with higher capacitance than traditional capacitors. Many approaches have been developed to optimize these experimental parameters by either the modification of the synthesis or the manufacturing process of the material, as a result, the variety of available options that can be explored has grown exponentially. Here, we present a review that summarizes the main mechanisms by which carbon supercapacitors work, the main modifications done to them, and the main carbon structures reported in the literature, present an overview of the latest advancements and emphasizing ones that seem to be giving better results. From this analysis, we can conclude that carbon has yet to achieve the highest capacitance results seen with other materials, but it has a definite advantage in the flexibility in production methods as well as in power and energy density. We can see that novel carbon structures present new opportunities to close the gap in capacitance and could take the lead in wearable and flexible supercapacitors.

Ciencias Exactas y Ciencias de la Salud

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