Engineering topics

Witman, Matthew D.

Publications and source records attributed to Witman, Matthew D..

5 records

DOE OSTI2024

A Bulk versus Nanoscale Hydrogen Storage Paradox Revealed by Material-System Co-Design

Metal hydrides are serious contenders for materials-based hydrogen storage to overcome constraints associated with compressed or liquefied H 2 . Their ultimate performance is usually evaluated using intrinsic material properties without considering a systems design perspective. An illustrative case with startling implications is (LiNH 2 +2LiH). Using models that simulate the storage system and associated fuel cell of a light-duty vehicle (LDV), the performance of the bulk hydrides is compared with a nanoscaled version in porous carbon (PC), (LiNH 2 +2LiH)@(6-nm PC). Using experimental material properties, the simulations show that (LiNH 2 +2LiH)@(6-nm PC) counterintuitively has higher usable gravimetric and volumetric capacities than the bulk counterpart on a system basis despite having lower capacities on a materials-only basis. Nanoscaling increases the thermal conductivity and lowers the desorption enthalpy, which consequently increases heat management efficiency. In a simulated drive cycle for fuel cell-powered LDV, the fuel cell is inoperable using bulk (LiNH 2 +2LiH) as the storage material but completes the drive cycle using the nanoscale material. Further, these results challenge the notion that nanoscaling incurs mass and volume penalties. Instead, the synergistic nanoporous host-hydride interaction can favorably modulate chemical and heat transfer properties. Moreover, a co-design approach considering application-specific tradeoffs is essential to accurately assess a material's potential for real-world hydrogen storage.

08 HYDROGEN↗

DOE OSTI2024

Tuning the Spin Transition and Carrier Type in Rare-Earth Cobaltates via Compositional Complexity

There is growing interest in material candidates with properties that can be engineered beyond traditional design limits. Compositionally complex oxides (CCO), often called high entropy oxides, are excellent candidates, wherein a lattice site shares more than four cations, forming single-phase solid solutions with unique properties. However, the nature of compositional complexity in dictating properties remains unclear, with characteristics that are difficult to calculate from first principles. Here, in this study, compositional complexity is demonstrated as a tunable parameter in a spin-transition oxide semiconductor La 1- x (Nd, Sm, Gd, Y) x/4 CoO 3 , by varying the population x of rare earth cations over 0.00≤ x≤ 0.80. Across the series, increasing complexity is revealed to systematically improve crystallinity, increase the amount of electron versus hole carriers, and tune the spin transition temperature and on-off ratio. At high a population (x = 0.8), Seebeck measurements indicate a crossover from hole-majority to electron-majority conduction without the introduction of conventional electron donors, and tunable complexity is proposed as new method to dope semiconductors. First principles calculations combined with angle resolved photoemission reveal an unconventional doping mechanism of lattice distortions leading to asymmetric hole localization over electrons. Thus, tunable complexity is demonstrated as a facile knob to improve crystallinity, tune electronic transitions, and to dope semiconductors beyond traditional means.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Defect graph neural networks for materials discovery in high-temperature clean-energy applications

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. In conclusion, it will help to tackle future materials discovery problems in clean energy and beyond.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Tuning the critical Li intercalation concentrations for MoX 2 bilayer phase transitions using classical and machine learning approaches

Transition metal dichalcogenides (TMDs) such as MoX 2 are known to undergo a structural phase transformation as well as a change in the electronic conductivity upon Li intercalation. These properties make them candidates for charge tunable ion-insertion materials that could be used in electro-chemical devices for neuromorphic computing applications. In this work we study the phase stability and electronic structure of Li-intercalated bilayer MoX 2 with X=S, Se or Te. Using first-principles calculations in combination with classical and machine learning modeling approaches we find that the energy needed to stabilize the conductive phase decreases with increasing atomic mass of the chalcogen atom X. A similar decreasing trend is found in the threshold Li concentration where the structural phase transition takes place. While the electronic conductivity increases with increasing ion concentration at low concentrations, we do not observe a conductivity jump at the phase transition point.

36 MATERIALS SCIENCE↗