  We present a deep-learning framework, CrysXPP, to allow rapid prediction of
electronic, magnetic and elastic properties of a wide range of materials with
reasonable precision. Although our work is consistent with several recent
attempts to build deep learning-based property predictors, it overcomes some of
their limitations. CrysXPP lowers the need for a large volume of tagged data to
train a deep learning model by intelligently designing an autoencoder CrysAE
and passing the structural information to the property prediction process. The
autoencoder in turn is trained on a huge volume of untagged crystal graphs, the
designed loss function helps in capturing all their important structural and
chemical information. Moreover, CrysXPP uses only a small amount of tagged data
for property prediction, and also trains a feature selector that provides
interpretability to the results obtained. We demonstrate that CrysXPP
convincingly performs better than all the competing and recent baseline
algorithms across seven diverse set of properties. Most notably, when given a
small amount of experimental data, CrysXPP is consistently able to outperform
conventional DFT. We release the large pretrained model CrysAE so that it could
be fine-tuned using small amount of tagged data by the research community on
various applications with restricted data source.
