Hydrogen uptake prediction in porous carbon materials explained by decision tree machine learning Algorithms: From experimental data to interpretable predictions

Widespread adoption of hydrogen fuel is constrained by the cost and safety limits of high-pressure and cryogenic storage. Adsorption-based storage in Porous Carbon Materials (PCMs) is a promising alternative, yet its potential is unrealized due to the research time and cost of discovery. A Machine L...

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Vydané v:	International journal of hydrogen energy Ročník 197; s. 152704
Hlavní autori:	Sunkara, Hemanth, Bhat A S, Shravani, R, Namitha, Acharya, Sushmitha, Shekar, Selva Kumar, Sainath, Krishnamurthy, Siddiqui, Shabnam
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 05.01.2026
Predmet:	Adsorption Decision trees Hydrogen Machine learning Porous carbon materials SHAP SHAP Adsorption Porous carbon materials Decision trees Hydrogen Machine learning
ISSN:	0360-3199
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