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.