The microscopic understanding of the dramatic increase in viscosity of liquids when cooled toward the glass transition is a major unresolved issue in condensed matter physics. Here, we use machine learning methods to accelerate molecular dynamics simulations with first-principles accuracy for the glass-former toluene. We show that the increase in viscosity is intimately linked to the increasing number of dynamically correlated molecules $N^{*}$. While certain hallmark features of glassy dynamics, like physical aging, are linked to $N^{*}$ as well, others, like relaxation stretching, are not.

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• Glassy systems
• First-principles calculations
• Machine learning