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Accelerating Quantum Chemistry Simulations with AI

Abe Stern, NVIDIA

GTC 2020

We'll discuss computational chemistry applications of machine learning covering three topics. First, we'll examine the use of neural networks and other machined-learned methods for describing a quantum-accurate potential energy surface. Second, we'll cover graph convolution neural networks and graph message-passing networks for predicting molecular properties at a fraction of the cost of traditional electronic structure calculations. Third, we'll discuss variational autoencoders for molecule discovery and illustrate their application to drug discovery.

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