The immense space of composition-processing parameters leads to numerous trial-and-error experimental works for engineering of novel bulk metallic glasses (BMGs). To tackle this challenging problem, it is required to consider specific guidelines which are able to restrict the productive alloying compositions. In this work, a correlation-based neural network (CBNN) approach was developed, based on a dataset of 7950 alloying compositions, to design potential new MGs through prediction of casting ability, reduced glass transition (T_rg) and critical thickness (D_max). This approach involves individual and mutual characteristics of contributory factors to improve the prediction accuracy. To validate our model, we selected the ZrCoAl alloying system and investigated the microalloying effects on the glass forming possibility (GFP). According to the results, the microalloying process effects strongly depended on the …