Classification of Amorphous Versus Crystalline Phases of Alloys Using Electrical Resistivity

Efficient identification of amorphous versus crystalline phases in multi-component alloys is crucial for the high-throughput development of new metallic glasses, yet it is often limited by the reliance on slow or expensive techniques like table-top or synchrotron-based X-ray diffraction (XRD). This study investigates electrical resistivity as a predictive descriptor for alloy phases due to its sensitivity to atomic arrangements and rapid measurability. Using a combinatorial magnetron sputtering process, hundreds of multi-component alloys with both amorphous and crystalline phases were synthesized, followed by XRD measurements. High-throughput electrical resistivity measurements of the combinatorial alloy thin films were performed using a custom-built robotic stage equipped with a 4-point probe. Artificial neural networks were then developed to correlate electrical resistivity data with X-ray diffractograms across a wide range of the combinatorially synthesized alloys. These machine learning models accurately classified amorphous and crystalline phases in both thin-film libraries and bulk alloys, offering a promising alternative to traditional phase identification methods.