Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability

Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet, the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial and error. Data-driven materials’ discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here, we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitate a data-driven approach that does not rely on exact crystal structures to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based on not only targeted thermodynamic properties but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70× increase in equilibrium pressure, 20 kJ/molH2 decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfHx. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient material selection for various applications, especially in areas such as metal hydride-based hydrogen compressors, actuators, and heat pumps

Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability

Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial-and-error. Data-driven materials\u27 discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitates a data-driven approach that does not rely on exact crystal structures in order to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based not only on targeted thermodynamic properties, but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70x increase in equilibrium pressure, 20 kJ/molH2 decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfHx. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient materials selection for various applications, especially in areas such as metal hydride based hydrogen compressors, actuators, and heat pumps

Towards Pareto optimal high entropy hydrides via data-driven materials discovery

The ability to rapidly screen material performance in the vast space of compositionally complex (high entropy) alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of 10,000s of candidate equimolar alloys. We critically show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials (i.e., where two or more competing objective properties can\u27t be simultaneously improved) and provide rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Selected target materials were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts

Hydrogen Storage in Partially Exfoliated Magnesium Diboride Multilayers

Metal boride nanostructures have shown significant promise for hydrogen storage applications. However, the synthesis of nanoscale metal boride particles is challenging because of their high surface energy, strong inter- and intraplanar bonding, and difficult-to-control surface termination. Here, it is demonstrated that mechanochemical exfoliation of magnesium diboride in zirconia produces 3-4 nm ultrathin MgB2 nanosheets (multilayers) in high yield. High-pressure hydrogenation of these multilayers at 70 MPa and 330 °C followed by dehydrogenation at 390 °C reveals a hydrogen capacity of 5.1 wt%, which is ≈50 times larger than the capacity of bulk MgB2 under the same conditions. This enhancement is attributed to the creation of defective sites by ball-milling and incomplete Mg surface coverage in MgB2 multilayers, which disrupts the stable boron-boron ring structure. The density functional theory calculations indicate that the balance of Mg on the MgB2 nanosheet surface changes as the material hydrogenates, as it is energetically favorable to trade a small number of Mg vacancies in Mg(BH4 )2 for greater Mg coverage on the MgB2 surface. The exfoliation and creation of ultrathin layers is a promising new direction for 2D metal boride/borohydride research with the potential to achieve high-capacity reversible hydrogen storage at more moderate pressures and temperatures