  We develop new transfer learning algorithms to accelerate prediction of
material properties from ab initio simulations based on density functional
theory (DFT). Transfer learning has been successfully utilized for
data-efficient modeling in applications other than materials science, and it
allows transferable representations learned from large datasets to be
repurposed for learning new tasks even with small datasets. In the context of
materials science, this opens the possibility to develop generalizable neural
network models that can be repurposed on other materials, without the need of
generating a large (computationally expensive) training set of materials
properties. The proposed transfer learning algorithms are demonstrated on
predicting the Gibbs free energy of light transition metal oxides.
