Data driven development of iron-based metallic glasses using artificial neural networks

Abstract

Metallic glasses exhibit outstanding properties due to their distinctive amorphous structure. Within the realm of metallic glasses, iron-based variants stand out as a cost-effective option. However, the conventional approach to discover metallic glass alloys is inefficient in regards to the required resources. Manufacturing processes for metallic glasses, such as melt spinning as well as the analysis by differential scanning calorimetry (DSC) are cost- and time-intensive. Artificial neural networks (ANN) are used to address this issue. The ANNs can predict the complex, non-linear behaviour of glass formation and therefore save time and resources. The database required for a Machine Learning approach was gathered through a literature research on iron-based metallic glasses. After hyperparameter tuning, the trained ANNs can estimate the Glass-Forming Ability (GFA) parameter γm of various alloy compositions with a Root Mean Squared Error of 0.0296. A Genetic Algorithm (GA) is incorporated in the ANN to find GFA maxima in defined ranges. This approach enables the optimisation of the GFA prior to experimentation and thus enhances the discovery of new iron-based metallic glass alloys. Using an ANN alleviates the need for in depth analysis and screening experimentation while still allowing a fast, resource efficient virtual alloy evaluation.