Conveners
Tutorial on the PANNA package for training and validating neural networks to represent atomic potentials: Lecture on theory & concepts
- Stefano de Gironcoli ()
Tutorial on the PANNA package for training and validating neural networks to represent atomic potentials: Hands-on session
- Stefano de Gironcoli ()
Tutorial on the PANNA package for training and validating neural networks to represent atomic potentials: Hands-on session
- Stefano de Gironcoli ()
Description
This tutorial cover the concepts and practical experience on using PANNA (Properties from Artificial Neural Network), a package for training and validating neural networks to represent atomic potentials. It implements configurable all-to-all connected deep neural network architectures which allow for the exploration of training dynamics. Currently it includes tools to enable original and modified Behler-Parrinello input feature vectors, both for molecules and crystals, but the network can also be used in an input-agnostic fashion to enable further experimentation. PANNA is written in Python and relies on TensorFlow as the underlying engine.
