  We have developed models of metallic alloy glass forming ability based on
newly computationally accessible features obtained from molecular dynamics
simulations. In this work we showed that it is possible to increase the
predictive value of GFA models by using input features obtained from molecular
dynamics simulations. Such features require only relatively straightforward and
scalable simulations, making them significantly easier and less expensive to
obtain than experimental measurements. We generated a database of molecular
dynamics critical cooling rates along with associated candidate features that
are inspired from previous research on GFA. Out of the list of 9 proposed GFA
features, we identify two as being the most important to performance through a
LASSO model. Enthalpy of crystallization and icosahedral-like fraction at 100 K
showed promise because they enable a significant improvement to model
performance and because they are accessible to flexible ab initio quantum
mechanical methods readily applicable to almost all systems. This advancement
in computationally accessible features for machine learning predictions GFA
will enable future models to more accurately predict new glass forming alloys.
