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2019 GTC San Jose

S9417 - Molecular Generative VAEs: Parallelization, Optimization, and Latent Space Analysis on the DGX-1

Session Speakers
Session Description

Generative Variational Autoencoders (VAE) in molecular discovery and new materials design have recently gained considerable attention in academia as well as industry (Gomez-Bombarelli, 2017). In this talk, we will present results from a combined Dow Chemical and NVIDIA development effort to implement a VAE for chemical discovery. We'll discuss challenges associated with applying deep learning to chemistry and highlight recently developed methods. Highlights from our presentation will include a discussion of methods to analyze and sample from an organized latent representation in a conditioned variational autoencoder, tips for training a complex architecture, distributed multi-node training using Horovod, and results showing the generation of molecular structure with associated property prediction.


Additional Information
Computational Biology/Chemistry
Advanced AI Learning Techniques (Incl. GANs/NTMs), Computational Biology/Chemistry
Manufacturing
Intermediate technical
Talk
50 minutes
Session Schedule