  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.
