  In order to make accurate predictions of material properties, current
machine-learning approaches generally require large amounts of data, which are
often not available in practice. In this work, an all-round framework is
presented which relies on a feedforward neural network, the selection of
physically-meaningful features and, when applicable, joint-learning. Next to
being faster in terms of training time, this approach is shown to outperform
current graph-network models on small datasets. In particular, the vibrational
entropy at 305 K of crystals is predicted with a mean absolute test error of
0.009 meV/K/atom (four times lower than previous studies). Furthermore,
joint-learning reduces the test error compared to single-target learning and
enables the prediction of multiple properties at once, such as temperature
functions. Finally, the selection algorithm highlights the most important
features and thus helps understanding the underlying physics.
