Hands-on Sequential Learning and Design of Experiments

Описание: 2020.04.20 By Juan Carlos Verduzco Gastelum and Alejandro Strachan, Purdue University

This video is a part of a hands-on machine learning and data science training workshop conducted by nanoHUB in April 2020.

Downloads for this presentation can be found at https://nanohub.org/resources/33267

This session introduces the concept of sequential learning and information acquisition functions and talks about how these can be applied to reduce the number of experiments to find an optimal candidate for a property. A hands-on approach is presented to optimize the ionic conductivity of ceramic materials. Tool used in this demonstration is Citrine Tools for Materials Informatics (https://nanohub.org/tools/citrinetools).