[Paper Review] Picturing the Gap Between the Performance and US-DOE's Hydrogen Storage Target: A Data-Driven Model for MgH2 Dehydrogenation

Chaoqun Li, Weijie Yang+6 more|arXiv (Cornell University)|Apr 16, 2024

Spacecraft and Cryogenic Technologies10 citations

TL;DR

The paper introduces a data-driven model to predict MgH2 dehydrogenation barriers using Mg-H bond orbital contributions and H-atom distance, offering a cheaper alternative to ab initio transition-state searches and aligning with US-DOE targets.

ABSTRACT

Developing solid-state hydrogen storage materials is as pressing as ever, which requires a comprehensive understanding of the dehydrogenation chemistry of a solid-state hydride. Transition state search and kinetics calculations are essential to understanding and designing high-performance solid-state hydrogen storage materials by filling in the knowledge gap that current experimental techniques cannot measure. However, the ab initio analysis of these processes is computationally expensive and time-consuming. Searching for descriptors to accurately predict the energy barrier is urgently needed, to accelerate the prediction of hydrogen storage material properties and identify the opportunities and challenges in this field. Herein, we develop a data-driven model to describe and predict the dehydrogenation barriers of a typical solid-state hydrogen storage material, magnesium hydride (MgH2), based on the combination of the crystal Hamilton population orbital of Mg-H bond and the distance between atomic hydrogen. By deriving the distance energy ratio, this model elucidates the key chemistry of the reaction kinetics. All the parameters in this model can be directly calculated with significantly less computational cost than conventional transition state search, so that the dehydrogenation performance of hydrogen storage materials can be predicted efficiently. Finally, we found that this model leads to excellent agreement with typical experimental measurements reported to date and provides clear design guidelines on how to propel the performance of MgH2 closer to the target set by the United States Department of Energy (US-DOE).

Motivation & Objective

• Motivate the need for faster descriptors to predict hydrogen storage material kinetics beyond costly ab initio transition-state searches.
• Develop a data-driven predictor for MgH2 dehydrogenation barriers.
• Show that the model agrees with experimental measurements and guides design toward DOE targets.

Proposed method

• Combine crystal Hamilton population analysis of the Mg-H bond with the H-atom distance to form a distance energy ratio.
• Derive a descriptor that correlates with reaction energy barriers.
• Ensure all parameters are computable with significantly lower cost than conventional transition-state searches.
• Validate the model by comparing predictions to available experimental data.

Experimental results

Research questions

• RQ1 Can a data-driven descriptor based on Mg-H bond characteristics and hydrogen distance accurately predict MgH2 dehydrogenation barriers?
• RQ2 Does the proposed descriptor align with experimental measurements and US-DOE hydrogen storage performance targets?
• RQ3 What design guidelines emerge from the descriptor to improve MgH2 dehydrogenation performance?
• RQ4 How does the model compare, in terms of computational cost and predictive accuracy, to traditional ab initio transition-state methods.

Key findings

• The model describes and predicts dehydrogenation barriers for MgH2 using the distance energy ratio tied to Mg-H bond orbital contributions and H distance.
• Predictions show excellent agreement with typical experimental measurements reported to date.
• The approach provides design guidelines to move MgH2 performance closer to US-DOE targets.
• All model parameters are computable with significantly lower computational cost than conventional transition-state searches.