Understanding Anharmonic Effects on Hydrogen Desorption Characteristics of Mg$_n$H$_{2n}$ Nanoclusters by ab initio trained Deep Neural Network

Pedrielli, Andrea; Trevisanutto, Paolo E.; Monacelli, Lorenzo; Garberoglio, Giovanni; Pugno, Nicola; Taioli, Simone

doi:10.48550/arxiv.2111.13956

2021

DOI: 10.48550/arxiv.2111.13956

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Understanding Anharmonic Effects on Hydrogen Desorption Characteristics of Mg$_n$H$_{2n}$ Nanoclusters by ab initio trained Deep Neural Network

Andrea Pedrielli¹,

Paolo E. Trevisanutto²,

Lorenzo Monacelli³

et al.

Abstract: Magnesium hydride (MgH 2 ) has been widely studied for effective hydrogen storage.However, its bulk desorption temperature (553 K) is deemed too high for practical applications. Besides doping, a strategy to decrease such reaction energy for releasing

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Understanding Anharmonic Effects on Hydrogen Desorption Characteristics of Mg$_n$H$_{2n}$ Nanoclusters by ab initio trained Deep Neural Network

Abstract: Magnesium hydride (MgH 2 ) has been widely studied for effective hydrogen storage.However, its bulk desorption temperature (553 K) is deemed too high for practical applications. Besides doping, a strategy to decrease such reaction energy for releasing

Cited by 0 publications

References 40 publications

No citations

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