  Predicting materials properties from composition or structure is of great
interest to the materials science community. Deep learning has recently
garnered considerable interest in materials predictive tasks with low model
errors when dealing with large materials data. However, deep learning models
suffer in the small data regime that is common in materials science. Here we
leverage the transfer learning concept and the graph network deep learning
framework and develop the AtomSets machine learning framework for consistent
high model accuracy at both small and large materials data. The AtomSets models
can work with both compositional and structural materials data. By combining
with transfer learned features from graph networks, they can achieve
state-of-the-art accuracy from using small compositional data (<400) to large
structural data (>130,000). The AtomSets models show much lower errors than the
state-of-the-art graph network models at small data limits and the classical
machine learning models at large data limits. They also transfer better in the
simulated materials discovery process where the targeted materials have
property values out of the training data limits. The models require minimal
domain knowledge inputs and are free from feature engineering. The presented
AtomSets model framework opens new routes for machine learning-assisted
materials design and discovery.
