Publications

The vast chemical space of high entropy alloys (HEAs) makes trial-and-error experimental approaches for materials discovery intractable and often necessitates data-driven and/or first principles computational insights to successfully target materials with desired properties. In the context of materials discovery for hydrogen storage applications, a theoretical prediction-experimental validation approach can vastly accelerate the search for substitution strategies to destabilize high-capacity hydrides based on benchmark HEAs, e.g. TiVNbCr alloys. Here, machine learning predictions, corroborated by density functional theory calculations, predict substantial hydride destabilization with increasing substitution of earth-abundant Fe content in the (TiVNb)75Cr25-xFex system. The as-prepared alloys crystallize in a single-phase bcc lattice for limited Fe content x < 7, while larger Fe content favors the formation of a secondary C14 Laves phase intermetallic. Short range order for alloys with x < 7 can be well described by a random distribution of atoms within the bcc lattice without lattice distortion. Hydrogen absorption experiments performed on selected alloys validate the predicted thermodynamic destabilization of the corresponding fcc hydrides and demonstrate promising lifecycle performance through reversible absorption/desorption. This demonstrates the potential of computationally expedited hydride discovery and points to further opportunities for optimizing bcc alloy ↔ fcc hydrides for practical hydrogen storage applications.

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Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

High-entropy alloys (HEAs) represent an interesting alloying strategy that can yield exceptional performance properties needed across a variety of technology applications, including hydrogen storage. Examples include ultrahigh volumetric capacity materials (BCC alloys → FCC dihydrides) with improved thermodynamics relative to conventional high-capacity metal hydrides (like MgH2), but still further destabilization is needed to reduce operating temperature and increase system-level capacity. In this work, we demonstrate efficient hydride destabilization strategies by synthesizing two new Al0.05(TiVNb)0.95-xMox (x = 0.05, 0.10) compositions. We specifically evaluate the effect of molybdenum (Mo) addition on the phase structure, microstructure, hydrogen absorption, and desorption properties. Both alloys crystallize in a bcc structure with decreasing lattice parameters as the Mo content increases. The alloys can rapidly absorb hydrogen at 25 °C with capacities of 1.78 H/M (2.79 wt %) and 1.79 H/M (2.75 wt %) with increasing Mo content. Pressure-composition isotherms suggest a two-step reaction for hydrogen absorption to a final fcc dihydride phase. The experiments demonstrate that increasing Mo content results in a significant hydride destabilization, which is consistent with predictions from a gradient boosting tree data-driven model for metal hydride thermodynamics. Furthermore, improved desorption properties with increasing Mo content and reversibility were observed by in situ synchrotron X-ray diffraction, in situ neutron diffraction, and thermal desorption spectroscopy.

The objective of this project was to evaluate material- and chemical-based solutions for hydrogen storage in rail applications as an alternative to high-pressure hydrogen gas and liquid hydrogen. Three use cases were assessed: yard switchers, long-haul locomotives, and tenders. Four storage options were considered: metal hydrides, nanoporous sorbents, liquid organic hydrogen carriers, and ammonia, using 700 bar compressed hydrogen as a benchmark. The results suggest that metal hydrides, currently the most mature of these options, have the highest potential. Storage in tenders is the most likely use case to be successful, with long-haul locomotives the least likely due to the required storage capacities and weight and volume constraints. Overall, the results are relevant for high-impact regions, such as the South Coast Air Quality Management District, for which an economical vehicular hydrogen storage system with minimal impact on cargo capacity could accelerate adoption of fuel cell electric locomotives. The results obtained here will contribute to the development of technical storage targets for rail applications that can guide future research. Moreover, the knowledge generated by this project will assist in development of material-based storage for stationary applications such as microgrids and backup power for data centers.

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98-119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. This framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.

Organic co-crystals have emerged as a promising class of semiconductors for next-generation optoelectronic devices due to their unique photophysical properties. This paper presents a joint experimental-theoretical study comparing the crystal structure, spectroscopy, and electronic structure of two charge transfer co-crystals. Reported herein is a novel co-crystal Npe:TCNQ, formed from 4-(1-naphthylvinyl)pyridine (Npe) and 7,7,8,8-tetracyanoquinodimethane (TCNQ) via molecular self-assembly. This work also presents a revised study of the co-crystal composed of Npe and 1,2,4,5-tetracyanobenzene (TCNB) molecules, Npe:TCNB, herein reported with a higher-symmetry (monoclinic) crystal structure than previously published. Npe:TCNB and Npe:TCNQ dimer clusters are used as theoretical model systems for the co-crystals; the geometries of the dimers are compared to geometries of the extended solids, which are computed with periodic boundary conditions density functional theory. UV-Vis absorption spectra of the dimers are computed with time-dependent density functional theory and compared to experimental UV-Vis diffuse reflectance spectra. Both Npe:TCNB and Npe:TCNQ are found to exhibit neutral character in the S0 state and ionic character in the S1 state. The high degree of charge transfer in the S1 state of both Npe:TCNB and Npe:TCNQ is rationalized by analyzing the changes in orbital localization associated with the S1 transitions.

The hydrogen sorption properties of single-phase bcc (TiVNb)100-xCrx alloys (x = 0-35) are reported. All alloys absorb hydrogen quickly at 25 °C, forming fcc hydrides with storage capacity depending on the Cr content. A thermodynamic destabilization of the fcc hydride is observed with increasing Cr concentration, which agrees well with previous compositional machine learning models for metal hydride thermodynamics. The steric effect or repulsive interactions between Cr-H might be responsible for this behavior. The cycling performances of the TiVNbCr alloy show an initial decrease in capacity, which cannot be explained by a structural change. Pair distribution function analysis of the total X-ray scattering on the first and last cycled hydrides demonstrated an average random fcc structure without lattice distortion at short-range order. If the as-cast alloy contains a very low density of defects, the first hydrogen absorption introduces dislocations and vacancies that cumulate into small vacancy clusters, as revealed by positron annihilation spectroscopy. Finally, the main reason for the capacity drop seems to be due to dislocations formed during cycling, while the presence of vacancy clusters might be related to the lattice relaxation. Having identified the major contribution to the capacity loss, compositional modifications to the TiVNbCr system can now be explored that minimize defect formation and maximize material cycling performance.

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The ability to rapidly screen material performance in the vast space of 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 tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ∼5 kJ molH2−1) 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 by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ molH2−1) and 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.

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The objective of this project was to evaluate material-based hydrogen storage solutions as a replacement for high-pressure hydrogen gas or liquid hydrogen on Class 7 or 8 tractor fuel cell electric vehicles. The project focused on low-density main-group hydrides, a well-known class of materials for hydrogen storage. Prior research has considered metal amides as storage materials for light-duty vehicles but not for heavy-duty applications. The project established the basis for further development of storage systems of this type for heavy duty vehicles (HDV). Systems analysis of an HDV storage system comprised of a tank and associated balance of plant (piping, coolant tubes, burner) was performed to determine the usable hydrogen capacity. A composite storage material comprised of a metal hydride mixed with a high thermal-conductivity carbon is predicted to have a usable hydrogen volumetric capacity comparable to or exceeding that of 700 bar pressurized hydrogen gas. The gravimetric capacity of this material is also predicted to be competitive with pressurized gas, particularly if costly carbon fiber composite Type III or Type IV tanks are excluded. The storage system design parameters and material properties served as inputs to a second model that simulates fuel cell operation in conjunction with the storage system during an HDV drive cycle. The results show that sufficient hydrogen pressure can be produced to operate a Class 8 HDV, yielding a range of ~480 miles. These results are particularly relevant for high-impact regions, such as the South Coast Air Quality Management District, for which an economical vehicular hydrogen storage system with minimal impact on cargo capacity could accelerate adoption of heavy-duty fuel cell electric vehicles. An additional benefit is that knowledge generated by this project can assist in development of material-based storage for stationary applications such as microgrids and backup power for data centers.

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Grignard reagents of the general formula RMgX (X = Cl-, Br-, I-) have been utilized in various chemistries for over 100 years. We report that replacing the halide in a Grignard reagent with a reactive borohydride anion adds a new synthetic dimension for these influential compounds. We synthesized the series RMgBH4 (R = Et, n-Bu, Ph, Bn) and characterized the reactivity toward both organic and inorganic molecules. Using butylmagnesium borohydride (BuMgBH4) as an exemplar, we demonstrate that these compounds possess unique reactivity due to the presence of reducing borohydride groups, resulting in tandem reactivity with organic amides/esters to generate secondary and primary alcohols. Molecular dynamics simulations indicate the stability of BuMgBH4 is comparable to that of Mg(BH4)2 + MgBu2, validating the Schlenk equilibrium in borohydride Grignard compounds. Metadynamics simulations confirm that the equilibrium is kinetically accessible through solvent-mediated processes. BuMgBH4 also reacts with CO2 and NH3, revealing potential uses for CO2 utilization and as a mixed-anion metal borohydride/amide precursor.

Physisorption of hydrogen in nanoporous materials offers an efficient and competitive alternative for hydrogen storage. At low temperatures (e.g. 77 K) and moderate pressures (below 100 bar) molecular H2 adsorbs reversibly, with very fast kinetics, at high density on the inner surfaces of materials such as zeolites, activated carbons and metal-organic frameworks (MOFs). This review, by experts of Task 40 ‘Energy Storage and Conversion based on Hydrogen’ of the Hydrogen Technology Collaboration Programme of the International Energy Agency, covers the fundamentals of H2 adsorption in nanoporous materials and assessment of their storage performance. The discussion includes recent work on H2 adsorption at both low temperature and high pressure, new findings on the assessment of the hydrogen storage performance of materials, the correlation of volumetric and gravimetric H2 storage capacities, usable capacity, and optimum operating temperature. The application of neutron scattering as an ideal tool for characterising H2 adsorption is summarised and state-of-the-art computational methods, such as machine learning, are considered for the discovery of new MOFs for H2 storage applications, as well as the modelling of flexible porous networks for optimised H2 delivery. The discussion focuses moreover on additional important issues, such as sustainable materials synthesis and improved reproducibility of experimental H2 adsorption isotherm data by interlaboratory exercises and reference materials.