On the basis of 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 at
half maximum (fwhm) of >0.42 Å–1 for the
first sharp X-ray diffraction peak. Proper phase labeling is important
for future machine learning efforts. We demonstrate that the fwhm
and corrosion resistance are correlated but that, while chemistry
still plays a role in corrosion resistance, a large fwhm, attributed
to a glassy phase, is necessary for the highest corrosion resistance.