Presentation Information
[16a-W8E_308-1]Temperature Dependence of Ternary Phase Diagrams Using Universal Machine Learning Inter-atomic Potential
〇Hiroki Kotaka1 (1.Matlantis Corp.)
Keywords:
Phase Diagram simulation,Universal Machine Learning Potential
In this study, we constructed temperature-dependent phase diagrams for In-Ga-As and In-Ga-Sb systems, accounting for both vibrational and configurational entropy to evaluate the thermal stability of their solid solution phases. The formation energies were calculated using a universal machine learning potential, PFP. Our simulations confirmed that the solid solution phases transition to stable states on the convex hull as temperature increases. Integrating this computational approach with experimental data, such as melting points, is expected to provide critical design guidelines for synthesizing high-quality mixed-crystal semiconductors with desired composition ratios.