Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecu- lar mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and which are, at the same time, sufficiently affordable hold the key to bridging this gap. In this work we present a robust ML potential for the EC:EMC binary solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries. We identify the necessary ingredients needed to successfully model this liquid mixture of or- ganic molecules. In particular, we address the chal- lenge posed by the separation of scale between intra- and inter-molecular interactions, which is a general is- sue in all condensed phase molecular systems.