The development of efficient hydrogen storage technologies is crucial for the transition to sustainable energy solutions. Hydrogen, known for its versatility and clean energy potential, can be produced from renewable sources. Solid-state hydrogen storage materials, such as magnesium hydride (MgH2), are considered promising due to their high storage capacity and availability. However, despite decades of research, MgH2 has not met the performance targets set by the US Department of Energy (US-DOE). The primary challenge lies in understanding the fundamental principles of solid-state hydrogen storage reactions.

Current methods for evaluating hydrogen storage materials rely on dehydrogenation enthalpy and energy barriers, with the latter being complex and computationally intensive to calculate. Traditional transition state search techniques are costly and time-consuming, hindering the discovery and optimization process. To address these issues, researchers have developed a data-driven model to predict dehydrogenation barriers in MgH2. This model utilizes easily computable parameters, such as the crystal Hamilton population orbital of the Mg–H bond and the distance between atomic hydrogen atoms, to derive a distance-energy ratio. By doing so, the model captures the essential chemistry of the reaction kinetics with significantly lower computational requirements than conventional methods.

The findings of this research have been published in the journal Angewandte Chemie International Edition. Associate professor Hao Li from Tohoku University’s Advanced Institute for Materials Research (WPI-AIMR) and corresponding author of the paper, states that the model offers a faster and more efficient way to predict the dehydrogenation performance of hydrogen storage materials. This advancement bridges the gap left by experimental techniques and accelerates the development of high-performance hydrogen storage solutions. The model’s predictive power has been validated against typical experimental measurements, demonstrating excellent agreement and providing clear design guidelines to enhance the performance of MgH2.

This breakthrough not only brings magnesium hydride closer to the US-DOE targets but also opens up possibilities for broader applications in other metal hydrides. The research team plans to extend the model’s application beyond magnesium-based materials. The flexibility of the model’s variables allows for rapid recalibration to different metal hydrides, potentially leading to the discovery of new composite materials and innovative solid-state hydrogen storage solutions. By adapting the model to various metal hydrides, the exploration and optimization of hydrogen storage materials can be expedited, paving the way for cleaner and more efficient energy systems.

The development of a data-driven model to predict dehydrogenation barriers in magnesium hydride represents a significant advancement in hydrogen storage technologies. This research not only enhances our understanding of solid-state hydrogen storage reactions but also offers a more efficient way to assess and improve the performance of hydrogen storage materials. With further exploration and optimization, this model has the potential to revolutionize the development of solid-state hydrogen storage solutions and accelerate the transition to cleaner and more sustainable energy systems.

Chemistry

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