  Various combinations of characteristic temperatures, such as the glass
transition temperature, liquidus temperature, and crystallization temperature,
have been proposed as predictions of the glass forming ability of metal alloys.
We have used statistical approaches from machine learning to systematically
explore a wide range of possible characteristic temperature functions for
predicting glass forming ability in the form of critical casting diameter,
$D_{max}$. Both linear and non-linear models were used to learn on the largest
database of $D_{max}$ values to date consisting of 747 compositions. We find
that no combination of temperatures for features offers a better prediction of
$D_{max}$ in a machine learning model than the temperatures themselves, and
that regression models suffer from poor performance on standard machine
learning metrics like root mean square error (minimum value of $3.3 \pm 0.1$
$mm$ for data with a standard deviation of 4.8 $mm$). Examination of the errors
vs. database size suggest that a larger database may improve results, although
a database significantly larger than that used here would likely be required.
Shifting a focus from regression to categorization models learning from
characteristic temperatures can be used to weakly distinguish glasses likely to
be above vs. below our database's median $D_{max}$ value of 4.0 $mm$, with a
mean F1 score of $0.77 \pm 0.02$ for this categorization. The overall weak
results on predicting $D_{max}$ suggests that critical cooling rate might be a
better target for machine learning model prediction.
