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GTC ON-DEMAND

AI Application, Deployment & Inference
Presentation
Media
ANI-AL: Universal Deep Learning Potentials for Organic Molecules and Materials
Abstract:
We'll introduce ANI-AL molecular potentials, which are deep learning based potential energy functions for the fast and accurate prediction of quantum mechanical energies and forces of molecular systems. Thanks to GPU acceleration of training and inference, we successfully implement an automated sampling method that borrows techniques from active learning to automatically drive the systematic improvement of ANI-AL potentials. We'll also present results from applications of the ANI-AL potential in various problems relating to computational chemistry, such as molecular structure optimization, reaction path prediction, vibrational frequency calculation, and molecular dynamics simulations.
 
Topics:
AI Application, Deployment & Inference, Computational Biology & Chemistry
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8827
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