High-Throughput Computation and Machine Learning in the Study of Mechanical Properties of Platinum Alloys: Advancements and Prospects

Platinum-based alloys have garnered increasing attention due to their exceptional high-temperature stability, corrosion resistance, and mechanical performance, enabling applications in aerospace, high-end equipment, and glass manufacturing. Recent studies have deepened understanding of representative Pt alloy systemsincluding binary, ternary, and multi-principal alloys—focusing on the interplay among thermodynamic stability, elastic response, and plastic deformation mechanisms. Key descriptors such as formation energy, elastic constants, and generalized stacking fault energy have been employed to characterize these behaviors. In parallel, machine learning approaches—including graph neural networks, physics-informed models, and active learning—have been utilized to improve predictive capability and interpretability. Despite these advances, challenges remain in achieving accurate performance predictions under small-sample, high-cost, and strongly coupled mechanistic conditions. Continued efforts in mechanism-driven descriptor design, multi-level modeling, and the integration of computational and experimental data are expected to enable the rational design of next-generation Pt alloys with tailored mechanical properties.