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Recent advances in artificial intelligence have propelled the development ofinnovative computational materials modeling and design techniques. Generativedeep learning models have been used for molecular representation, discovery,and design. In this work, we assess the predictive capabilities of a moleculargenerative model developed based on variational inference and graph theory inthe small data regime. Physical constraints that encourage energetically stablemolecules are proposed. The encoding network is based on the scatteringtransform with adaptive spectral filters to allow for better generalization ofthe model. The decoding network is a one-shot graph generative model thatconditions atom types on molecular topology. A Bayesian formalism is consideredto capture uncertainties in the predictive estimates of molecular properties.The model's performance is evaluated by generating molecules with desiredtarget properties.

Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder