Predicting multi-component, high-temperature metallic glasses by coupling empirical models, CALPHAD, and a genetic algorithm

Metallic glasses (MGs) are an emerging class of materials possessing multiple desirable properties including high strength, hardness, and corrosion resistance when compared to their crystalline counterparts. However, most previously studied MGs are not useful in high temperature environments because they undergo the glass transition phenomenon and crystallize below the melting point. In addition, bulk MGs are typically found in multi-component systems, meaning that searching compositional space with a reasonable resolution using computational or experimental methods can be costly. In this study, an in-house developed genetic algorithm-based tool was used to locate alloy compositions with high glass forming ability (GFA) and high-temperature stability in the Ta-Ni-Co-(B) and Ta-Ni-Co-Nb alloy systems. GFA was predicted using an empirical predictive parameter known as P HSS. High-temperature stability was predicted using the CALPHAD method to calculate liquidus temperature. Justification for the use of P HSS to predict GFA of high-temperature MGs, as well as the use of liquidus temperature as a predictor of general high-temperature stability, is given in the form of a meta-analysis of previously reported MG compositions. The predictions made using this algorithm were analyzed and are presented herein. While high-temperature stability was the property of interest for this research, this framework could be used in the future to locate alloys with other application-specific material properties. This genetic algorithm-based tool enables the coupling of empirical parameters and CALPHAD to efficiently search multi-component space to locate glass-forming alloys with desirable properties.