What question did this study set out to answer?

This research aims to explore the use of machine learning models to predict and optimize hydrogen storage capacity in solid-state materials.

June 13, 2026Next MaterialsOpen Access

Machine learning-driven optimization of solid-state hydrogen storage materials for sustainable energy applications

View Full Paper

Ask AI

Bookmark

Authors

QMQueen Moses PAP. Ashwath SPSudhagar Pitchaimuthu

Discussion

Member takes

Overview

Randomized trial investigates machine learning's role in enhancing hydrogen storage capacity in solid-state materials, indicating significant efficiency improvements.

Key Points

This research aims to explore the use of machine learning models to predict and optimize hydrogen storage capacity in solid-state materials.
A dataset of 239 hydrogen storage materials was compiled and analyzed using machine learning approaches.
Three models were applied: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR).
Performance metrics included R² score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).
The RF model outperformed other models with an R² score of 0.8657, MAE of 0.7102, and RMSE of 1.0187.
Sensitivity analysis revealed that material type and adsorption pressure are critical for hydrogen uptake prediction.
Complex hydrides showed the highest storage capacity, achieving up to 18.5 wt%.

Cite This Study

Moses et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf403faef96ed7f05658e https://doi.org/10.1016/j.nxmate.2026.102465

View Full Paper

Ask AI

Bookmark

June 13, 2026Next MaterialsOpen Access

Machine learning-driven optimization of solid-state hydrogen storage materials for sustainable energy applications

View Full Paper

Ask AI

Bookmark

Authors

QMQueen Moses PAP. Ashwath SPSudhagar Pitchaimuthu

Discussion

Member takes

Overview

Randomized trial investigates machine learning's role in enhancing hydrogen storage capacity in solid-state materials, indicating significant efficiency improvements.

Key Points

This research aims to explore the use of machine learning models to predict and optimize hydrogen storage capacity in solid-state materials.
A dataset of 239 hydrogen storage materials was compiled and analyzed using machine learning approaches.
Three models were applied: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR).
Performance metrics included R² score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).
The RF model outperformed other models with an R² score of 0.8657, MAE of 0.7102, and RMSE of 1.0187.
Sensitivity analysis revealed that material type and adsorption pressure are critical for hydrogen uptake prediction.
Complex hydrides showed the highest storage capacity, achieving up to 18.5 wt%.

Cite This Study

Moses et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf403faef96ed7f05658e https://doi.org/10.1016/j.nxmate.2026.102465

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark