| Abstract Scope |
The liquidus curve captures the high-temperature eutectics, peritectics, congruent melting and incongruent melting regions of a phase diagram. Being able to predict liquidus curves would enable ab initio guidance of materials synthesis temperatures, as well as the design of materials stable under high-temperature operation conditions. Here, we present a CALPHAD-inspired approach to reference liquidus curves from published ASM phase diagrams to DFT convex hulls from the Materials Project. Using this technique, we fit non-ideal liquid mixing free-energies on a 70x70 matrix of binary alloy phase diagrams. Using machine-learning models, we then predict liquid free energies in novel chemical spaces, including ternary or quaternary+ spaces, at a computational cost low-enough to be integrated with high-throughput DFT databases like the Materials Project. Our technique predicts liquidus curves, intermetallic melting temperatures, and three-phase invariant points with good accuracy, despite the known magnitude of formation energy errors in DFT convex hulls. |