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.