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Free Hands-On Workshop on Machine Learning Offered by nanoHUB, July 21, 2021

nanoHUB has announced a new workshop that will be offered in the Spring 2021 session of the Hands-on Data Science and Machine Learning Training Series. 

Series information: The series is aimed at active researchers and educators and designed to introduce practical skills with online, hands-on activities that participants will be able to incorporate into their own work. Hands-on activities will use nanoHUB cloud computing resources, negating the need to download or install any software. All that is required of the audience is an internet connection and an hour to spare for the demonstration. After the training sessions, you will be able to continue using nanoHUB for research or education.

Recordings and associated materials from prior workshops can be found at the workshop webpage: https://nanohub.org/groups/ml/handsontraining

Register soon as seats are limited. 

Date/Time: Wednesday, July 21, 2021 / 1:30 PM – 2:30 PM EDT
Title: A Machine Learning aided hierarchical screening strategy for materials discovery
Presenter: Anjana Talapatra, Postdoctoral Fellow, Los Alamos National Laboratory

Register for this workshop here

Abstract:  One of the most basic approaches to problem-solving is to conceptualize the problem at different abstraction levels and translate from one abstraction level to the others easily, i.e., deal with them hierarchically. This concept is especially applicable to the field of novel materials discovery, wherein large candidate domains can be quickly and efficiently explored by hierarchically discarding irrelevant candidates. In this tutorial, we illustrate this approach using the example of wide band gap oxide perovskites. We will sequentially search a very large domain space of single and double oxide perovskites to identify candidates that are likely to be formable, thermodynamically stable, exhibit insulator nature and have a wide band gap. To this end, we will build four machine learning (ML) models: three classification and one regression model using experimental and DFT-calculated training data. The tutorial will discuss best practices for building ML models, commonly encountered pitfalls and how best to avoid them.

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