  The development by machine learning of models predicting materials'
properties usually requires the use of a large number of consistent data for
training. However, quality experimental datasets are not always available or
self-consistent. Here, as an alternative route, we combine machine learning
with high-throughput molecular dynamics simulations to predict the Young's
modulus of silicate glasses. We demonstrate that this combined approach offers
excellent predictions over the entire compositional domain. By comparing the
performance of select machine learning algorithms, we discuss the nature of the
balance between accuracy, simplicity, and interpretability in machine learning.
