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Successful computer studies of glass-forming materials need to overcome boththe natural tendency to structural ordering and the dramatic increase ofrelaxation times at low temperatures. We present a comprehensive analysis ofeleven glass-forming models to demonstrate that both challenges can beefficiently tackled using carefully designed models of size polydispersesupercooled liquids together with an efficient Monte Carlo algorithm wheretranslational particle displacements are complemented by swaps of particlepairs. We study a broad range of size polydispersities, using both discrete andcontinuous mixtures, and we systematically investigate the role of particlesoftness, attractivity and non-additivity of the interactions. Each system ischaracterized by its robustness against structural ordering and by theefficiency of the swap Monte Carlo algorithm. We show that the combinedoptimisation of the potential's softness, polydispersity and non-additivityleads to novel computer models with excellent glass-forming ability. For suchmodels, we achieve over ten orders of magnitude gain in the equilibrationtimescale using the swap Monte Carlo algorithm, thus paving the way tocomputational studies of static and thermodynamic properties under experimentalconditions. In addition, we provide microscopic insights into the performanceof the swap algorithm which should help optimizing models and algorithms evenfurther.

Models and algorithms for the next generation of glass transition studies