Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. In this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies Delta H V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe2O4 was identified as a promising material for experimental validation based on its predicted Delta H V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe2-x Al x O4; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

Solar thermochemical hydrogen (STCH) production uses concentrated sunlight to produce hydrogen using reduction/oxidation of metal oxides. Typically, the metal oxide is heated to high temperature (>1400 o C) causing it to release oxygen, then it is cooled to a lower temperature (~<1000 o C) in steam whereby it re-oxidizes, stripping oxygen from water molecules and producing hydrogen. Due to improved stability, non-stoichiometric oxides that do not change phase during the STCH process are typically used, even though the reversible oxygen content is less than phase changing materials. In this presentation, recent progress in developing and experimentally validating new water splitting materials using a defect Graph Neural Network trained on crystal structures derived from density functional theory will be discussed. An experimental screening protocol used to evaluate predicted materials will be presented. Additionally, durability challenges and pathways to testing in reactors on-sun that meet DOE hydrogen production targets will be discussed. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. Part of the work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract no. DE-AC52-07NA27344.

Phase coexistence in nanoscale electrochemical random-access memory (ECRAM) has recently been demonstrated to enable both information storage and extraordinary reconfigurability. These proof-of-principle demonstrations have left the mechanistic details of such a process unresolved. Particularly, the mechanisms that stabilize the multiple phases, and the underlying processes behind sustained memory retention, remain unclear, and are necessary to design such devices. Here we report microscale ECRAM devices composed of VOx, which enables us to directly probe the active region in an operando fashion using optical techniques. Using Raman mapping, we show the phase coexistence driven by the electrochemical injection of O vacancies to be spatially uniform (i.e., with no filaments). The stability was observed to be unusually long, with 1% loss over 14 years in ambient conditions. First-principles calculations of the oxygen vacancy formation energies in VOx further support the thermodynamic coexistence of multiple VOx phases and clarify the origin of the observed long-term retention in the ECRAM devices. Further, we demonstrate single devices that can be voltage programmed to exhibit synaptic, neuronal, and reconfigurable logic gate functionalities. Therefore, we not only uncover the phase coexistence mechanism that may help device design, but also demonstrate the circuit-level applications of reconfigurability.

Formation and migration energies of crystallographic defects dictate material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based calculations are a powerful computational technique for predicting defect formation energies and migration energy barriers, yet they can become prohibitively expensive for high-throughput materials discovery exercises. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors and using only pristine host structures as input, we propose generalized structure-property defect models by hybridizing defect graph neural networks with transformer encoders to predict these properties. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for thermochemical water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be rapidly identified and experimentally validated. Figure 1

Phenomenological CALPHAD (CALculation of PHAse Diagrams) models, widely used for multicomponent materials, often contain a considerable number of parameters and require fitting using data from a relatively small number of experimental measurements or theoretical calculations. Sometimes these parameters are introduced for the purpose of improving model fits but without clear physical justification, which leads to over-parameterized models with poor generalization performance. Automated approaches for optimal model selection based on the available data therefore become critical. In this work, a least absolute shrinkage and selection operator (LASSO)-based approach is developed for model selection by leveraging the linearity of the CALPHAD model with respect to its parameters to convert the model selection and fitting to a LASSO minimization problem. We demonstrate its utility for thermodynamic modeling of thermochemical hydrogen (TCH) production materials, using lanthanum strontium manganite (LSM) as an example. Various TCH relevant properties, including oxygen stoichiometry as function of oxygen partial pressure, enthalpy of reduction, and entropy of reduction, are successfully predicted with reasonable accuracy using a minimal set of model parameters. Importantly, the model selection and fitting involve minimal human decision; it can therefore be applied to high-throughput DFT defect calculations and yield efficient workflows for TCH materials modeling and optimization.

Lattice-core sandwich structure metamaterials are lightweight alternatives to monolithic materials that can present better mechanical, thermal, and energy dampening performance. Manufacturing lattice metamaterials to follow curved surfaces can pose a challenge, as the lattices rely on their geometric orientation to the substrate for their mechanical properties. This work rationally designed a lattice structure where the surface is broken up into “petals” connected to the underlying lattice, which localizes the petals’ impact response. This design opens a pathway for implementation of lattice-core sandwich structures onto complex surface geometries. These petal structures were evaluated for their energy absorption efficiency experimentally by utilizing pressure waves generated with nanosecond lasers and computationally via finite element modeling. The lattice structures exhibited a two-orders-of-magnitude decrease in transmitted pressure compared to their constituent steel at equivalent mass. Furthermore, localizing energy absorption into petal structures provided a 44% reduction in peak load compared to a continuous “single-petal” design.

Electrical polarization and defect transport are examined in 0.8BaTiO(3)-0.2BiZn(0.5)Ti(0.5)O(3), an attractive capacitor material for high power electronics. Oxygen vacancies are suggested to be the majority charge carrier at or below 250 degrees C with a grain conduction hopping activation energy of 0.97 eV and 0.92 eV for thermally stimulated depolarization current (TSDC) and impedance spectroscopy measurements, respectively. At higher temperature, thermally generated electronic conduction with an activation energy of 1.6 eV is dominant. Significant oxygen vacancy concentration is indicated (up to similar to 1%) due to cation vacancy formation (i.e., acceptor defects) from observed Bi (and likely Zn) volatility. Oxygen vacancy diffusivity is estimated to be 10(-12.8) cm(2)/s at 250 degrees C. Low diffusivity and high activation energies are indicative of significant defect interactions. Dipolar oxygen vacancy defects are also indicated, with an activation energy of 0.59 eV from TSDC measurements. The large oxygen vacancy content leads to a short lifetime during high voltage (30 kV/cm), high temperature (250 degrees C) direct current (DC) electrical measurements.