[BNC-all] hands-on workshop of machine learning

[Turner, Jaime J]

Dear Colleagues,

nanoHUB is excited to announce the next workshop in the Summer 2021 session of our Hands-on Data Science and Machine Learning Training Series.

Series information. Our 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 in 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.

Registration links and material for prior workshops can be found at the workshop webpage: https://nanohub.org/groups/ml/handsontraining

Please register soon as seats are limited. Share with any colleagues who may be interested.

Title: A Machine Learning aided hierarchical screening strategy for materials discovery
Date: July 21st, 2021, 1:30 PM - 2:30 PM EST
Speaker: Anjana Talapatra, Postdoctoral Fellow, Los Alamos National Laboratory
Register here (limited seats): https://nanohub.org/groups/ml/handsontraining

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.




Alejandro Strachan
Deputy Director, nanoHUB (2020 R&D 100 winner)


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Alejandro Strachan
Professor of Materials Engineering, Purdue University
Network for Computational Nanotechnology - nanohub.org<http://nanohub.org>
Center for Predictive Materials and Devices (c-PRIMED)

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