  Future advancement of engineering applications is dependent on design of
novel materials with desired properties. Enormous size of known chemical space
necessitates use of automated high throughput screening to search the desired
material. The high throughput screening uses quantum chemistry calculations to
predict material properties, however, computational complexity of these
calculations often imposes prohibitively high cost on the search for desired
material. This critical bottleneck is resolved by using deep machine learning
to emulate the quantum computations. However, the deep learning algorithms
require a large training dataset to ensure an acceptable generalization, which
is often unavailable a-priory. In this paper, we propose a deep Gaussian
process based approach to develop an emulator for quantum calculations. We
further propose a novel molecular descriptor that enables implementation of the
proposed approach. As demonstrated in this paper, the proposed approach can be
implemented using a small dataset. We demonstrate efficacy of our approach for
prediction of formation energy of inorganic molecules.
