Mark Donald Allendorf

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although artificial intelligence (AI) has accelerated materials discovery, current approaches remain constrained by fragmented data, limited physical consistency, and weak integration with experimental validation. Here, we propose a unified framework that integrates coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design within a closed-loop discovery paradigm. By constraining optimization with thermodynamics, kinetics, uncertainty, provenance, and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs.

Nanoconfinement of metastable hydrides such as alane (AlH₃) in functionalized porous frameworks represents a promising yet largely untapped strategy for high-capacity energy storage. Despite its potential, the underlying mechanisms responsible for the thermodynamic stabilization of metastable hydrides are poorly understood. Here, concepts from solution Lewis acid-base chemistry were applied to a crystalline metal-organic framework (MOF) to stabilize AlH₃. The long-range order and synthetically versatile pore chemistry enabled us to reveal the intimate details of the hydride-host interactions. Powder X-ray diffraction (PXRD) of AlH₃-infiltrated UiO-67bpy (Zr₆O₄(OH)₄(bpydc)₆; bpydc^2- = 2,2'-bipyridine-5,5'-dicarboxylate) confirms that the framework maintains its crystallinity up to 250 °C and is stable under 70 MPa H₂ pressure. We find that thermodynamic stabilization arises from coupled single-electron and hydrogen-atom transfer from AlH₃ to bipyridine-containing linkers. Electron paramagnetic resonance detects a bipyridyl radical with an anisotropic g-tensor (g values of 2.00132, 2.00215, and 2.00275), consistent with Bader charge analysis predicting 0.82 e^- transferred from the hydride to the MOF. These findings establish critical structure-property relationships underpinning AlH₃ stabilization and suggest general strategies for tuning the thermodynamics and kinetics of metastable materials.

Quantitatively accurate computational predictions of metal hydride thermodynamics are challenging but critical for alloy performance optimization across a multitude of technological domains, including hydrogen storage, compression, purification, and getters. Recent machine learning approaches have demonstrated great success in this area, but can potentially suffer from several shortcomings since they rely on imbalanced experimental training data and can have poor out-of-distribution (ood) test performance. Here we circumvent such pitfalls by developing a computationally efficient, first principles-based workflow for direct prediction of metal hydride phase equilibrium, i.e., the pressure-composition-temperature (PCT) diagram. We then demonstrate its utility on predicting low stability hydrides derived from compositionally complex C14 Laves phase AB₂ alloys. Specifically, we computationally predict and then experimentally validate an AB₂ alloy series (z < 0.6 for Ti_2−zZr_zCrMnFeNi) with ideal hydriding thermodynamics for a two-stage metal hydride-based compressor for pressurizing boil off from liquefied hydrogen. Importantly, this study lays the groundwork for accurate and efficient discovery/optimization of ood, low-stability hydrides for which purely data-driven approaches lack sufficient accuracy.

A complex portfolio of hydrogen storage materials is currently under investigation for their potential use in self-sufficient, long duration energy storage (LDES) systems. However, variations in storage system design for even the same carrier material adds noise at the system-design level that makes it hard to derive material-level technical targets, let alone a “winner” material. Herein, we combine techno-economic and Monte Carlo analysis in a cross-comparison approach to identify the probability of low-cost hydrogen storage for LDES using: sorbents, metal hydrides, carriers, or compressed gas storage. Certain conditions allow any of these technologies to achieve the lowest levelised cost of storage (LCOS), and yet results suggest that 1,4-butanediol, involving a liquid phase hydrogenation and vapor phase dehydrogenation design, offers the highest probability of low LCOS at $0.19/kWhe ($6.40/kg), in comparison to 170 bar compressed gas at $0.24/kWhe ($8.04/kg) at the 10 MW scale (10-100 hours). Such insights are critical for guiding material selection for specific deployment locations and markets, and offer the detail necessary to consider new aspects of storage, from job creation, to safety, to operational complexity.

Body-centered cubic (BCC) based multi-principal element alloy (MPEA) hydrides have demonstrated significant potential for compact and efficient hydrogen storage.

Quantitatively accurate computational predictions of metal hydride thermodynamics are challenging but critical for alloy performance optimization across a multitude of technological domains, including hydrogen storage, compression, purification, and getters. Recent machine learning approaches have demonstrated great success in this area, but can potentially suffer from several shortcomings since they rely on imbalanced experimental training data and can have poor out-of-distribution (ood) test performance. Here we circumvent such pitfalls by developing a computationally efficient, first principles-based workflow for direct prediction of metal hydride phase equilibria, i.e., the pressure-composition-temperature (PCT) diagram. We then demonstrate its utility on predicting low stability hydrides derived from compositionally complex C14 Laves phase AB2 alloys. Specifically, we computationally predict and then experimentally validate an AB2 alloy series (z < 0.6 for Ti_{2−z}Zr_{z}CrMnFeNi) with ideal hydriding thermodynamics for a two-stage metal hydride-based compressor for pressurizing boil off from liquefied hydrogen. Importantly, this study lays the groundwork for accurate and efficient discovery/optimization of ood, low-stability hydrides for which purely data-driven approaches lack sufficient accuracy.