  We report a deep generative model for regression tasks in materials
informatics. The model is introduced as a component of a data imputer, and
predicts more than 20 diverse experimental properties of organic molecules. The
imputer is designed to predict material properties by "imagining" the missing
data in the database, enabling the use of incomplete material data. Even
removing 60% of the data does not diminish the prediction accuracy in a model
task. Moreover, the model excels at extrapolation prediction, where target
values of the test data are out of the range of the training data. Such
extrapolation has been regarded as an essential technique for exploring novel
materials, but has hardly been studied to date due to its difficulty. We
demonstrate that the prediction performance can be improved by >30% by using
the imputer compared with traditional linear regression and boosting models.
The benefit becomes especially pronounced with few records for an experimental
property (< 100 cases) when prediction would be difficult by conventional
methods. The presented approach can be used to more efficiently explore
functional materials and break through previous performance limits.
