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Data-driven predictions of metal hydride thermodynamic properties elucidate the Pareto optimal front of high entropy alloy candidates for hydrogen storage.

In recent years, it has been found that small weight percent additions of silicon to HA can be used to enhance the initial response between bone tissue and HA. A large amount of research has been concerned with bulk materials, however, only recently has the attention moved to the use of these doped materials as coatings. This paper focusses on the development of a co-RF and pulsed DC magnetron sputtering methodology to produce a high percentage Si containing HA (SiHA) thin films (from 1.8 to 13.4 wt.%; one of the highest recorded in the literature to date). As deposited thin films were found to be amorphous, but crystallised at different annealing temperatures employed, dependent on silicon content, which also lowered surface energy profiles destabilising the films. X-ray photoelectron spectroscopy (XPS) was used to explore the structure of silicon within the films which were found to be in a polymeric (SiO2; Q4) state. However, after annealing, the films transformed to a SiO44−, Q0, state, indicating that silicon had substituted into the HA lattice at higher concentrations than previously reported. A loss of hydroxyl groups and the maintenance of a single-phase HA crystal structure further provided evidence for silicon substitution. Furthermore, a human osteoblast cell (HOB) model was used to explore the in vitro cellular response. The cells appeared to prefer the HA surfaces compared to SiHA surfaces, which was thought to be due to the higher solubility of SiHA surfaces inhibiting protein mediated cell attachment. The extent of this effect was found to be dependent on film crystallinity and silicon content.

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't 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.