Algorithmic identification

Researchers at the UCLA Henry Samueli School of Engineering and Applied Science have developed a computer model that could accelerate the development of hydrogen-fuelled vehicles by identifying promising hydrogen-storage materials and predicting favoured thermodynamic chemical reactions through which hydrogen can be reversibly stored and extracted.

The new method was developed by Alireza Akbarzadeh, a UCLA postdoctoral researcher in the department of materials science and engineering, Vidvuds Ozolins, UCLA associate prof of materials science and engineering and Christopher Wolverton, prof of materials science and engineering at Northwestern University in Illinois.

Widespread adoption of hydrogen as a fuel has been hindered by the need to store it on-board at very high densities. A promising solution involves storing hydrogen within a material in the form of a chemically bound hydride, for example lithium hydride (LiH). Simple binary hydrides - in which hydrogen combines with light elements such as lithium, sodium, magnesium or others - do not adequately satisfy the requirements for on-board storage, as the hydrogen-yielding reaction requires heating the material to impractically high temperatures.

Because of this, researchers have turned to multicomponent hydride mixtures with higher volumetric and gravimetric densities, better operating temperatures and improved reaction rates for practical hydrogen storage. However, this flexibility comes at the price of drastically increased complexity associated with the large number of competing reactions and possible end-products other than hydrogen.

Thus, predicting desirable hydrogen storage with multicomponent mixtures has proved difficult.

For example, the recently studied lithium hydride compound Li4BN3H10 was found to have as many as 17 hydrogen-release reactions, of which only three were found to be feasible - and none were in the desired range of temperatures and hydrogen pressures for practical on-board storage in hydrogen-powered vehicles.

To help out, the UCLA research team developed an algorithm that can automatically and systematically pinpoint phases and reactions that have the most favoured thermodynamic properties - that is, those that can release hydrogen at ambient temperatures using the waste heat from a proton exchange membrane (PEM) fuel cell.

The team tested the method on the well-studied Lithium-Magnesium-Nitrogen-Hydrogen system, predicting all experimentally observed pathways in the system. The researchers said this method can also be applied to other multicomponent hydrogen systems.

'The development of an algorithm that goes beyond chemical intuition and finds all hydrogen storage reactions ‘in silico’ is crucial and will help the scientific and engineering community to develop revolutionary new hydrogen-storage materials,' said Akbarzadeh. 'This is a major achievement in the field, which can help in the search for the best reversible solid-state hydrogen storage.'