  Despite increasing focus on data publication and discovery in materials
science and related fields, the global view of materials data is highly sparse.
This sparsity encourages training models on the union of multiple datasets, but
simple unions can prove problematic as (ostensibly) equivalent properties may
be measured or computed differently depending on the data source. These hidden
contextual differences introduce irreducible errors into analyses,
fundamentally limiting their accuracy. Transfer learning, where information
from one dataset is used to inform a model on another, can be an effective tool
for bridging sparse data while preserving the contextual differences in the
underlying measurements. Here, we describe and compare three techniques for
transfer learning: multi-task, difference, and explicit latent variable
architectures. We show that difference architectures are most accurate in the
multi-fidelity case of mixed DFT and experimental band gaps, while multi-task
most improves classification performance of color with band gaps. For
activation energies of steps in NO reduction, the explicit latent variable
method is not only the most accurate, but also enjoys cancellation of errors in
functions that depend on multiple tasks. These results motivate the publication
of high quality materials datasets that encode transferable information,
independent of industrial or academic interest in the particular labels, and
encourage further development and application of transfer learning methods to
materials informatics problems.
