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

• URL: http://arxiv.org/abs/2009.13878v2
• Date: Thu, 11 Feb 2021 09:18:13 GMT
• Title: Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
• Authors: Navid Shervani-Tabar, Nicholas Zabaras
• Abstract summary: We develop a molecular generative model based on variational inference and graph theory in the small data regime. The model's performance is evaluated by generating molecules with desired target properties.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in artificial intelligence have propelled the development of innovative computational materials modeling and design techniques. Generative deep learning models have been used for molecular representation, discovery, and design. In this work, we assess the predictive capabilities of a molecular generative model developed based on variational inference and graph theory in the small data regime. Physical constraints that encourage energetically stable molecules are proposed. The encoding network is based on the scattering transform with adaptive spectral filters to allow for better generalization of the model. The decoding network is a one-shot graph generative model that conditions atom types on molecular topology. A Bayesian formalism is considered to capture uncertainties in the predictive estimates of molecular properties. The model's performance is evaluated by generating molecules with desired target properties.

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