Physics-guided inverse design of Co-based superalloys using machine learning and multi-objective optimization for enhanced gamma-prime solvus temperature
Materials
Presents a physics-informed ML framework for inverse design of Co-based superalloys, using atomic size mismatch and other descriptors with multi-objective optimization to enhance gamma-prime solvus temperature while constraining density.
See LICENSE file Co-based superalloygamma-prime solvus temperatureML inverse design