  Glasses form the basis of many modern applications and also hold great
potential for future medical and environmental applications. However, their
structural complexity and large composition space make design and optimization
challenging for certain applications. Of particular importance for glass
processing is an estimate of a given composition's glass-forming ability (GFA).
However, there remain many open questions regarding the physical mechanisms of
glass formation, especially in oxide glasses. It is apparent that a proxy for
GFA would be highly useful in glass processing and design, but identifying such
a surrogate property has proven itself to be difficult. Here, we explore the
application of an open-source pre-trained NN model, GlassNet, that can predict
the characteristic temperatures necessary to compute glass stability (GS) and
assess the feasibility of using these physics-informed ML (PIML)-predicted GS
parameters to estimate GFA. In doing so, we track the uncertainties at each
step of the computation - from the original ML prediction errors, to the
compounding of errors during GS estimation, and finally to the final estimation
of GFA. While GlassNet exhibits reasonable accuracy on all individual
properties, we observe a large compounding of error in the combination of these
individual predictions for the prediction of GS, finding that random forest
models offer similar accuracy to GlassNet. We also breakdown the ML performance
on different glass families and find that the error in GS prediction is
correlated with the error in crystallization peak temperature prediction.
Lastly, we utilize this finding to assess the relationship between
top-performing GS parameters and GFA for two ternary glass systems: sodium
borosilicate and sodium iron phosphate glasses. We conclude that to obtain true
ML predictive capability of GFA, significantly more data needs to be collected.
