A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al

TL;DR

This study combines machine learning predictions with high-throughput experiments to analyze glass formation and corrosion resistance in the Ni-Ti-Al metallic glass system, revealing key structural and electrochemical relationships.

Contribution

It introduces a dual-modality high-throughput approach integrating machine learning, synthesis, and characterization to classify amorphous materials and understand their corrosion properties.

Findings

FWHM of 0.42 A$^{-1}$ indicates amorphous structure

FWHM correlates with corrosion resistance

Chemistry influences but does not solely determine corrosion resistance

Abstract

Based on a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high-throughput structural and electrochemical characterization. Using this dual-modality approach, we are able to better classify the amorphous portion of the library, which we found to be the portion with a full-width-half-maximum (FWHM) of 0.42 A$^{-1}$ for the first sharp x-ray diffraction peak. We demonstrate that the FWHM and corrosion resistance are correlated but that, while chemistry still plays a role, a large FWHM is necessary for the best corrosion resistance.