  Metallic glasses have attracted considerable interest in recent years due to
their unique combination of superb properties and processability. Predicting
bulk metallic glass formers from known parameters remains a challenge and the
search for new systems is still performed by trial and error. It has been
speculated that some sort of "confusion" during crystallization of the
crystalline phases competing with glass formation could play a key role. Here,
we propose a heuristic descriptor quantifying confusion and demonstrate its
validity by detailed experiments on two well-known glass forming alloy systems.
With the insight provided by these results, we develop a robust model for
predicting glass formation ability based on the spectral decomposition of
geometrical and energetic features of crystalline phases calculated ab-initio
in the AFLOW high throughput framework. Our findings indicate that the
formation of metallic glass phases could be a much more common phenomenon than
currently estimated, with more than 17% of binary alloy systems being potential
glass formers. Our approach is capable of pinpointing favorable compositions,
overcoming a major bottleneck hindering the discovery of new materials. Hence,
it is demonstrated that smart descriptors, based solely on the energetics and
structure of competing crystalline phases calculated from first-principles and
available in online databases, others the sought-after key for accelerated
discovery of novel metallic glasses.
