|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
||Accelerated Discovery of Multi-phase Refractory Alloys through Machine Learning Surrogate Models of CALPHAD
||Samuel Price, Ian McCue, Jonah Erlebacher
|On-Site Speaker (Planned)
There is a great need for alloys that can operate at higher temperatures than Ni-base superalloys. Refractory alloys (e.g., consisting of Nb, Ta, Mo, and W) are promising alternatives, but their single-phase microstructures impair high-temperature mechanical performance. Thus, there is an impetus to design multi-phase refractory alloys with coherent precipitates that are stable to high temperatures. The vast compositional design space for such alloys necessitates the use of computational tools. One such tool, CAlculation of PHAse Diagrams (CALPHAD), can be used to screen alloys by predicting their equilibrium phases; however, CALPHAD is too slow to screen these large datasets. Here, we have solved this issue by developing accurate surrogate models of CALPHAD using artificial neural networks. Our new approach is 20x faster than the traditional “brute force” method, making it possible to search larger alloy systems in a matter of hours and days rather than weeks and months.
||High-Temperature Materials, Machine Learning, Phase Transformations