A Review of High Density Solid Hydrogen Storage Materials by Pyrolysis for Promising Mobile Applications

Huang, Yuansheng; Cheng, Yonghong; Zhang, Jinying

doi:10.1021/acs.iecr.0c04387

Cited by 64 publications

(30 citation statements)

References 334 publications

(670 reference statements)

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“…The addition of a third element to the binary hydrides can potentially stabilize these materials at much lower pressure [13][14][15] . Complex ternary and quaternary materials systems are also promising candidates for hydrogen storage applications 16 .…”

Section: Introductionmentioning

confidence: 99%

Data-augmentation for graph neural network learning of the relaxed energies of unrelaxed structures

2022

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Computational materials discovery has grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms (CSPA). However, the computational cost of the ab initio calculations required by CSPA limits its utility to small unit cells, reducing the compositional and structural space the algorithms can explore. Past studies have bypassed unneeded ab initio calculations by utilizing machine learning to predict the stability of a material. Specifically, graph neural networks trained on large datasets of relaxed structures display high fidelity in predicting formation energy. Unfortunately, the geometries of structures produced by CSPA deviate from the relaxed state, which leads to poor predictions, hindering the model’s ability to filter unstable material. To remedy this behavior, we propose a simple, physically motivated, computationally efficient perturbation technique that augments training data, improving predictions on unrelaxed structures by 66%. Finally, we show how this error reduction can accelerate CSPA.

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Section: Introductionmentioning

confidence: 99%

Data-augmentation for graph neural network learning of the relaxed energies of unrelaxed structures

2022

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“…Fossil fuels, such as coal, gas, and oil, are finite, non-renewable resources, and a current estimation based on their ongoing burning rate points to a complete depletion by the end of 2060. While they are more reliable than currently engineered materials, they also pollute the environment and are responsible in great part for the greenhouse effect [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. While the risk of running out of fossil fuels has been advocated for decades, the timeline is approaching and humanity needs to make a change.…”

Section: Introductionmentioning

confidence: 99%

“…First (IA) and second group (IIA) metal borohydrides are not only known, but also commercially available. Various reviews have tackled different areas regarding complex borohydrides and hydrogen, focusing either on current technologies [ 1 , 2 , 6 ] and conceptual design of storage materials [ 3 , 4 , 5 , 15 ], or on various forms of hydrogen carriers [ 7 , 8 ] utilized to eventually switch the aging and depleting conventional fuel to a “green” fuel [ 9 ]. Other reviews evaluate the role of nanostructured metal hydride species for hydrogen storage [ 10 , 17 ], and usually focus on the potential presented by light metal borohydrides [ 10 , 16 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Complex Metal Borohydrides: From Laboratory Oddities to Prime Candidates in Energy Storage Applications

Comănescu

2022

Materials

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Despite being the lightest element in the periodic table, hydrogen poses many risks regarding its production, storage, and transport, but it is also the one element promising pollution-free energy for the planet, energy reliability, and sustainability. Development of such novel materials conveying a hydrogen source face stringent scrutiny from both a scientific and a safety point of view: they are required to have a high hydrogen wt.% storage capacity, must store hydrogen in a safe manner (i.e., by chemically binding it), and should exhibit controlled, and preferably rapid, absorption–desorption kinetics. Even the most advanced composites today face the difficult task of overcoming the harsh re-hydrogenation conditions (elevated temperature, high hydrogen pressure). Traditionally, the most utilized materials have been RMH (reactive metal hydrides) and complex metal borohydrides M(BH4)x (M: main group or transition metal; x: valence of M), often along with metal amides or various additives serving as catalysts (Pd2+, Ti4+ etc.). Through destabilization (kinetic or thermodynamic), M(BH4)x can effectively lower their dehydrogenation enthalpy, providing for a faster reaction occurring at a lower temperature onset. The present review summarizes the recent scientific results on various metal borohydrides, aiming to present the current state-of-the-art on such hydrogen storage materials, while trying to analyze the pros and cons of each material regarding its thermodynamic and kinetic behavior in hydrogenation studies.

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“…The addition of a third element to the binary hydrides can potentially stabilize these materials at much lower pressure [13][14][15]. Complex ternary and quaternary materials systems are also promising candidates for hydrogen storage applications [16].…”

Section: Introductionmentioning

confidence: 99%

Data-Augmentation for Graph Neural Network Learning of the Relaxed Energies of Unrelaxed Structures

Gibson¹,

Hire²,

Hennig³

2022

Preprint

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Computational materials discovery has continually grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms (CSPA). However, the computational cost of the ab initio calculations required by CSPA limits its utility to small unit cells, reducing the compositional and structural space the algorithms can explore. Past studies have bypassed many unneeded ab initio calculations by utilizing machine learning methods to predict formation energy and determine the stability of a material. Specifically, graph neural networks display high fidelity in predicting formation energy. Traditionally graph neural networks are trained on large data sets of relaxed structures. Unfortunately, the geometries of unrelaxed candidate structures produced by CSPA often deviate from the relaxed state, which leads to poor predictions hindering the model's ability to filter energetically unfavorable prior to ab initio evaluation. This work shows that the prediction error on relaxed structures reduces as training progresses, while the prediction error on unrelaxed structures increases, suggesting an inverse correlation between relaxed and unrelaxed structure prediction accuracy. To remedy this behavior, we propose a simple, physically motivated, computationally cheap perturbation technique that augments training data to improve predictions on unrelaxed structures dramatically. On our test set consisting of 623 Nb-Sr-H hydride structures, we found that training a crystal graph convolutional neural networks, utilizing our augmentation method, reduced the MAE of formation energy prediction by 66% compared to training with only relaxed structures. We then show how this error reduction can accelerates CSPA by improving the model's ability to filter out energetically unfavorable structures accurately.

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A Review of High Density Solid Hydrogen Storage Materials by Pyrolysis for Promising Mobile Applications

Cited by 64 publications

References 334 publications

Data-augmentation for graph neural network learning of the relaxed energies of unrelaxed structures

Data-augmentation for graph neural network learning of the relaxed energies of unrelaxed structures

Complex Metal Borohydrides: From Laboratory Oddities to Prime Candidates in Energy Storage Applications

Data-Augmentation for Graph Neural Network Learning of the Relaxed Energies of Unrelaxed Structures

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