  We explore the use of characteristic temperatures derived from molecular
dynamics to predict aspects of metallic Glass Forming Ability (GFA).
Temperatures derived from cooling curves of self-diffusion, viscosity, and
energy were used as features for machine learning models of GFA. Multiple
target and model combinations with these features were explored. First, we use
the logarithm of critical casting thickness, $log_{10}(D_{max})$, as the target
and trained regression models on 21 compositions. Application of 3-fold
cross-validation on the 21 $log_{10}(D_{max})$ alloys showed only weak
correlation between the model predictions and the target values. Second, the
GFA of alloys were quantified by melt-spinning or suction casting amorphization
behavior, with alloys that showed crystalline phases after synthesis classified
as Poor GFA and those with pure amorphous phases as Good GFA. Binary GFA
classification was then modeled using decision tree-based methods (random
forest and gradient boosting models) and were assessed with nested-cross
validation. The maximum F1 score for the precision-recall with Good Glass
Forming Ability as the positive class was $0.82 \pm 0.01$ for the best model
type. We also compared using simple functions of characteristic temperatures as
features in place of the temperatures themselves and found no statistically
significant difference in predictive abilities. Although the predictive ability
of the models developed here are modest, this work demonstrates clearly that
one can use molecular dynamics simulations and machine learning to predict
metal glass forming ability.
