Seeking Metal-Organic Frameworks for hydrogen storage using classical and quantum active learning

Maicon Pierre Lourenço; Rishabh Shukla; Daya Ram Gaur; U.P. Singh; Dennis R. Salahub; Mosayeb Naseri; Sergey Gusarov

doi:10.1039/d5cp02747k

January 1, 2025Physical Chemistry Chemical Physics

Seeking Metal-Organic Frameworks for hydrogen storage using classical and quantum active learning

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Key Points

The study focuses on identifying efficient metal-organic frameworks for hydrogen storage applications.
Key findings emphasize the significance of active learning techniques in optimizing MOF structures for enhanced hydrogen absorption.
Approach combines classical and quantum active learning methods to accelerate the discovery process in material science.
These findings may lead to the development of advanced MOFs, making hydrogen a more viable energy source.

Implication

This analysis demonstrates effective hydrogen storage solutions using MOFs, highlighting classical and quantum active learning methods.

Abstract

Metal-organic frameworks (MOFs) are porous materials with applications from chemical sensing to gas storage and separation. The development of MOFs for hydrogen storage is highly desired. Hydrogen is a clean...

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Cite This Study

Lourenço et al. (2025) studied this question.

synapsesocial.com/papers/68de5d9383cbc991d0a20155 https://doi.org/10.1039/d5cp02747k

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Seeking Metal-Organic Frameworks for hydrogen storage using classical and quantum active learning

Key Points

Implication

Abstract

Discussion

Cite This Study

Seeking Metal-Organic Frameworks for hydrogen storage using classical and quantum active learning

Key Points

Implication

Abstract

Discussion

Cite This Study

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