A new class of castable cerium strengthened aluminum alloys has phenomenal high temperature properties without the need for heat treatment.

Single crystalline bulk and epitaxially grown gallium oxide (β–Ga₂O₃) was irradiated by 0.6 and 1.9 MeV protons to doses ranging from 5 × 10⁹ to 6 × 10¹⁴ cm⁻² in order to study the impact on charge carrier concentration and electrically active defects. Samples irradiated to doses at or above 2 × 10¹³ cm⁻² showed a complete removal of free charge carriers in their as-irradiated state, whereas little or no influence was observed below doses of 6 × 10¹² cm⁻². From measurements at elevated temperatures, a thermally activated recovery process is seen for the charge carriers, where the activation energy for recovery follow a second-order kinetics with an activation energy of ∼1.2 eV. Combining the experimental results with hybrid functional calculations, we propose that the charge carrier removal can be explained by Fermi-level pinning far from the conduction band minimum (CBM) due to gallium interstitials (Ga_i), vacancies (V_Ga), and antisites (Ga_O), while migration and subsequent passivation of V_Ga via hydrogen-derived or V_O defects may be responsible for the recovery. Following the recovery, deep level transient spectroscopy (DLTS) reveals generation of two deep levels, with energy positions around 0.75 and 1.4 eV below the CBM. Of these two levels, the latter is observed to disappear after the initial DLTS measurements, while the concentration of the former increases. We discuss candidate possibilities and suggest that the origins of these levels are more likely due to a defect complex than an isolated point defect.

Predicting phase stability in high entropy alloys (HEAs), such as phase fractions as functions of composition and temperature, is essential for understanding alloy properties and screening desirable materials. Traditional methods like CALPHAD are computationally intensive for exploring high-dimensional compositional spaces. To address such a challenge, this study explored and compared the effectiveness of random forests (RF) and deep neural networks (DNN) for accelerating materials discovery by building surrogate models of phase stability prediction. For interpolation scenarios (testing on the same order of system as trained), RF models generally produce smaller errors than DNN models. However, for extrapolation scenarios (training on lower-order systems and testing on higher order systems), DNNs generalize more effectively than traditional ML models. DNN demonstrate the potential to predict topologically relevant phase composition when data were missing, making it a powerful predictive tool in materials discovery frameworks. The study uses a CALPHAD dataset of 480 million data points generated from a custom database, available for further model development and benchmarking. Experiments show that DNN models are data-efficient, achieving similar performance with a fraction of the dataset. This work highlights the potential of DNNs in materials discovery, providing a powerful tool for predicting phase stability in HEAs, particularly within the Cr-Hf-Mo-Nb-Ta-Ti-V-W-Zr composition space.