Ying Yang

Carbide and nitride coatings are of interest to enable high performance particle fuel forms. The chemical compatibility of the candidate boron nitride (BN) against silicon carbide (SiC), elemental carbon and against the metals molybdenum (Mo), zirconium (Zr), aluminum (Al), and vanadium (V) was evaluated using the CALPHAD method. For each binary join, pseudo-binary isopleths and isothermal ternary sections of the governing system (B–C–N, B–metal–N, or V–Si–C) were computed at 2000, 1000, and 500 K, and interface stability was assessed from whether the two end-members share a direct two-phase tie-line. BN/SiC and C/BN are predicted to be mutually compatible across the calculated temperature window, with intermediate phases (Si3N4, B4C) appearing only at off-stoichiometric compositions or the lowest temperatures. In contrast, every metal–ceramic couple examined is reactive: Mo, Zr, and Al each reduce BN to form metal borides plus a metal nitride, and V reacts with both SiC (forming V silicides and carbides) and BN (forming V borides and VN). Direct metal/ceramic contact is therefore not thermodynamically stable, and kinetics of the interfacial reaction is needed to confirm the risk of the predicted reaction products.

A refined understanding of the thermomechanical response of HT9 ferritic-martensitic steel cladding during accident conditions is essential for defining future safety and operational efficiency limits in sodium fast reactors. Accident-type thermal transients can drive HT9 into the austenite phase region, where phase stability strongly influences mechanical behavior and thus accident progression. In this study, transient burst testing was performed at 0.5 °C/s heating rate with hoop stresses from 25 to 125 MPa, combined with digital image correlation to capture in-situ strain behavior. A pronounced strain-rate acceleration near 800 °C during the burst test corresponded to the onset of austenite formation and carbide dissolution predicted by thermodynamic modeling and shown by electron microscopy, while a subsequent strain-rate decrease near ~865 °C reflected volumetric contraction associated with the body-centered to face-centered cubic transformation. Comparison with high-temperature tensile data showed that ultimate tensile strength trends remained predictive of burst behavior, whereas yield strength exhibited a plateau near the onset of austenite stability. These results provide experimental evidence linking phase stability to mechanical response during rapid heating and offer new insights that can be incorporated into material models for ferritic-martensitic steels to improve cladding performance predictions and strengthen fuel performance analysis under accident conditions.

We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys.

We present four open-source datasets that provide results of density functional theory (DFT) calculations of ground-state properties of refractory solid solution binary alloys niobium-tantalum (NbTa), niobium-vanadium (NbV), tantalum-vanadium (TaV), and ternary alloys NbTaV ordered in body-centered-cubic (BCC) structures with 128 Bravais lattice sites. The first-principles code used to run the calculations is the Vienna Ab-Initio Simulation Package. The calculations have been collected by uniformly sampling chemical compositions across the entire compositional range. For each chemical composition, the calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. This sampling methodology resulted in running DFT simulations for a total of 3,100 randomized atomic configurations over 31 chemical compositions for each of the three binary alloys Nb-Ta, Nb-V, Ta-V, and a total of 10,500 randomized atomic structures over 105 chemical compositions for the ternary alloys Nb-Ta-V. For each atomic configuration, geometry optimization has been performed, and the data released contains information about each step of geometry optimization for each atomic configuration.

We present four open-source datasets that provide results of density functional theory (DFT) calculations of ground-state properties of refractory solid solution binary alloys niobium-tantalum (NbTa), niobium-vanadium (NbV), tantalum-vanadium (TaV), and ternary alloys NbTaV ordered in body-centered-cubic (BCC) structures with 128 Bravais lattice sites. The first-principles code used to run the calculations is the Vienna Ab-Initio Simulation Package. The calculations have been collected by uniformly sampling chemical compositions across the entire compositional range. For each chemical composition, the calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. This sampling methodology resulted in running DFT simulations for a total of 3,100 randomized atomic configurations over 31 chemical compositions for each of the three binary alloys Nb-Ta, Nb-V, Ta-V, and a total of 10,500 randomized atomic structures over 105 chemical compositions for the ternary alloys Nb-Ta-V. For each atomic configuration, geometry optimization has been performed, and the data released contains information about each step of geometry optimization for each atomic configuration.