  Data driven generative machine learning models have recently emerged as one
of the most promising approaches for new materials discovery. While the
generator models can generate millions of candidates, it is critical to train
fast and accurate machine learning models to filter out stable, synthesizable
materials with desired properties. However, such efforts to build supervised
regression or classification screening models have been severely hindered by
the lack of unstable or unsynthesizable samples, which usually are not
collected and deposited in materials databases such as ICSD and Materials
Project (MP). At the same time, there are a significant amount of unlabelled
data available in these databases. Here we propose a semi-supervised deep
neural network (TSDNN) model for high-performance formation energy and
synthesizability prediction, which is achieved via its unique teacher-student
dual network architecture and its effective exploitation of the large amount of
unlabeled data. For formation energy based stability screening, our
semi-supervised classifier achieves an absolute 10.3\% accuracy improvement
compared to the baseline CGCNN regression model. For synthesizability
prediction, our model significantly increases the baseline PU learning's true
positive rate from 87.9\% to 97.9\% using 1/49 model parameters.
  To further prove the effectiveness of our models, we combined our
TSDNN-energy and TSDNN-synthesizability models with our CubicGAN generator to
discover novel stable cubic structures. Out of 1000 recommended candidate
samples by our models, 512 of them have negative formation energies as
validated by our DFT formation energy calculations. Our experimental results
show that our semi-supervised deep neural networks can significantly improve
the screening accuracy in large-scale generative materials design.
