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We present a graph neural network approach that fully automates the prediction of defect formation enthalpies for any crystallographic site from the ideal crystal structure, without the need to create defected atomic structure models as input. Here we used density functional theory reference data for vacancy defects in oxides, to train a defect graph neural network (dGNN) model that replaces the density functional theory supercell relaxations otherwise required for each symmetrically unique crystal site. Interfaced with thermodynamic calculations of reduction entropies and associated free energies, the dGNN model is applied to the screening of oxides in the Materials Project database, connecting the zero-kelvin defect enthalpies to high-temperature process conditions relevant for solar thermochemical hydrogen production and other energy applications. The dGNN approach is applicable to arbitrary structures with an accuracy limited principally by the amount and diversity of the training data, and it is generalizable to other defect types and advanced graph convolution architectures. It will help to tackle future materials discovery problems in clean energy and beyond.

Metal hydrides (MHs) are promising candidates for storing hydrogen at ambient conditions at high volumetric energy densities. Recent developments suggest hydride‐based systems can cycle and operate at favorable pressures and temperatures that work well with fuel cells used in stationary power applications. In this study, we present a comprehensive design and cost analysis of MH‐based long duration hydrogen storage facilities for a variety of power end users (0 to 20 megawatts (MW) supplied over 0 to 100 hours), to offer insights on technical targets for material development and operation strategies. Our findings indicate that hydride‐based storage systems hold significant size advantage in physical footprint, requiring up to 65% less land than 170‐bar compressed gas storage. Metal hydride systems can be cost competitive with 350‐bar compressed gas systems, with TiFe0.85Mn0.05 achieving$0.45/kWh and complex MH Mg(NH2)2‐2.1LiH‐0.1KH achieving $ 0.38/kWh. Extending charging times and increasing operating cycles significantly reduce levelized cost of storage, especially for complex MHs. Key strategies to further enhance the competitiveness of MHs include leveraging waste heat from fuel cells, reducing use of critical minerals, and achieving MH production costs of US$10/kg. Stationary hydrogen storage is essential for enabling the use of hydrogen and fuel cell technologies in applications such as backup power supply. This work evaluates the benefits of metal hydrides for storage, demonstrating the impacts of system design and operation, and material properties on viability. The analysis framework can be used to evaluate new materials and selection of practical applications.

IRMOF-74 analogues are among the most widely studied metal-organic frameworks (MOFs) for adsorption applications because of their one-dimensional channels and high metal density. Most studies involving the IRMOF-74 series assume that the crystal lattice is rigid. This assumption guides the interpretation of experimental data, as changes in the crystal symmetry have so far been ignored as a possibility in the literature. Here, we report a deformation pattern, induced by the adsorption of argon, for IRMOF-74-V. This work has two main implications. First, we use molecular simulations to demonstrate that the IRMOF-74 series undergoes a deformation that is similar to the mechanism behind breathing MOFs, but is unique because the deformation pattern extends beyond a single unit cell of the original structure. Second, we provide an alternative interpretation of experimental small-angle X-ray scattering profiles of these systems, which changes how we view the fundamentals of adsorption in this MOF series. IRMOF-74 materials have thus far been thought to undergo only simple crystal lattice expansion upon gas adsorption. Here, Smit and co-workers demonstrate that these MOFs undergo a unique complex deformation upon argon uptake, changing how we view the fundamentals of adsorption in this series.

Generative diffusion models (DMs) for inorganic crystalline materials are being actively investigated for their potential to expand the chemical and structural design spaces for known functional materials. Generative candidates are particularly useful for applications where few functional, let alone commercially viable, materials currently exist, such as metal oxides for thermochemical water-splitting, which have strict requirements for defect thermodynamics and host stability. Here, we critically examine generated metal oxides from the MatterGen DM conditioned on select chemical systems for thermochemical water splitting applications. Perhaps most notably, we find that MatterGen predicts a novel, thermodynamically stable, quinary metal oxide, Ba2SrInFeO6, although this compound represents an ordered and layered substitution within the same A3B2O6 structural prototype as its two ternary end members. Detailed density functional theory calculations and spin configuration sampling for this material and its possible decomposition products—beyond what existed in MatterGen training data—are required to quantitatively validate hull energy predictions and conclusions of stability. Furthermore, the material exhibits oxygen defect formation energies appropriate for thermochemical water splitting, warranting targeted investigation in an experimental validation campaign, along with other future MatterGen candidates in this application space.

Hydrogen has the highest gravimetric energy density of any energy carrier and produces water as the only oxidation product, making it extremely attractive for both transportation and stationary power applications. However, its low volumetric energy density causes considerable difficulties, inspiring intense efforts to develop chemical-based storage using metal hydrides, liquid organic hydrogen carriers and sorbents. The controlled uptake and release of hydrogen by these materials can be described as a series of challenges: optimal properties fall within a narrow range, can only be found in few materials and often involve important trade-offs. In addition, a greater understanding of the complex kinetics, mass transport and microstructural phenomena associated with hydrogen uptake and release is needed. The goal of this Perspective is to delineate potential use cases, define key challenges and show that solutions will involve a nexus of several subdisciplines of chemistry, including catalysis, data science, nanoscience, interfacial phenomena and dynamic or phase-change materials. Hydrogen, which possesses the highest gravimetric energy density of any energy carrier, is attractive for both mobile and stationary power, but its low volumetric energy density poses major storage and transport challenges. This Perspective delineates potential use cases and defines the challenges facing the development of materials for efficient hydrogen storage.

Graph neural networks (GNNs) present a promising route for machine learning of solid-state materials’ properties, but methods capable of directly predicting defect properties from ideal, defect-free structures are needed. A GNN developed for direct defect property predictions enables high-throughput screening of redox-active oxides for energy applications and beyond.

Metal hydrides (MHs) are promising candidates for storing hydrogen at ambient conditions at high volumetric energy densities. Recent developments suggest hydride-based systems can cycle and operate at favorable pressures and temperatures that work well with fuel cells used in stationary power applications. In this study, we present a comprehensive design and cost analysis of MH-based long duration hydrogen storage facilities for a variety of power end users (0 to 20 megawatts (MW) supplied over 0 to 100 hours), to offer insights on technical targets for material development and operation strategies. Our findings indicate that hydride-based storage systems hold significant size advantage in physical footprint, requiring up to 65% less land than 170-bar compressed gas storage. Metal hydride systems can be cost competitive with 350-bar compressed gas systems, with TiFe0.85Mn0.05 achieving $0.45/kWh and complex MH Mg(NH2)2-2.1LiH-0.1KH achieving $$0.38/kWh. Extending charging times and increasing operating cycles significantly reduce levelized cost of storage, especially for complex MHs. Key strategies to further enhance the competitiveness of MHs include leveraging waste heat from fuel cells, reducing use of critical minerals, and achieving MH production costs of US$10/kg.

Generative diffusion models (DMs) for inorganic crystalline materials are being actively investigated for their potential to expand the chemical and structural design spaces for known functional materials. Generative candidates are particularly useful for applications where few functional, let alone commercially viable, materials currently exist, such as metal oxides for thermochemical water-splitting, which have strict requirements for defect thermodynamics and host stability. Here, we critically examine generated metal oxides from the MATTERGEN DM conditioned on select chemical systems for thermochemical water splitting applications. Perhaps most notably, we find that MATTERGEN predicts a novel, thermodynamically stable, quinary metal oxide, Ba2SrInFeO6, although this compound represents an ordered and layered substitution within the same A3B2O6 structural prototype as its two ternary end members. Detailed density functional theory calculations and spin configuration sampling for this material and its possible decomposition products—beyond what existed in MATTERGEN training data—are required to quantitatively validate hull energy predictions and conclusions of stability. Furthermore, the material exhibits oxygen defect formation energies appropriate for thermochemical water splitting, warranting targeted investigation in an experimental validation campaign, along with other future MATTERGEN candidates in this application space.