Yashaswi Pathak received his MS  Dual Degree in Computational Natural Sciences (CNS). His research work was supervised by Prof. Deva Priyakumar. Here’s a summary of Yashaswi Pathak’s thesis Deep learning Enabled Molecule Design as explained by him: 

The advent of modern machine learning and deep learning methods have revolutionized various fields such as computer vision, natural language processing, speech recognition, etc. In the the past decade these methods have made a profound impact in the field of natural science. Particularly, the computations involved in the field of molecular science have been greatly transformed with the advent of machine learning. These changes have resulted in tremendous reduction of time and cost involved in tasks such as molecular generation and property prediction, which are two crucial steps involved in the process of molecular design. In this thesis, two studies on in each of these is reported. First part of the thesis presents a deep learning based inorganic material generator (DING) framework consisting of a generator module and a predictor module. The generator module is developed based upon conditional variational autoencoders (CVAE) and the predictor module consists of three deep neural networks trained for predicting enthalpy of formation, volume per atom and energy per atom chosen to demonstrate the proposed method. The predictor and generator modules have been developed using a one-hot key representation of the material composition. A series of tests were done to examine the robustness of the predictor models, to demonstrate the continuity of the latent material space, and its ability to generate materials exhibiting target property values. The DING architecture proposed in this paper can be extended to other properties based on which the chemical space can be efficiently explored for interesting materials/molecules. Second part of the thesis presents an interpretable deep learning method based on graph networks that accurately predict solvation free energies of small organic molecules. The proposed model CIGIN, comprising of three phases, namely message passing, interaction and prediction is able to predict solvation free energies in any generic organic solvent with a mean absolute error of 0.16 kcal/mol. In terms of the accuracy, the CIGIN model outperforms all the proposed machine learning based models so far. The atomic interactions predicted in an unsupervised manner is able to explain the trends of free energies consistent with chemical wisdom. Further, the robustness of the machine learning based model has been tested thoroughly and its capability to interpret the predictions has been verified with several examples.