Artificial Intelligence Toolkit - Workshop
January 7, 2021 hands-on session for the on-line course on big data and artificial intelligence in materials sciences
Exploratory analyses make use of unsupervised learning techniques to extract information from unknown datasets. In this tutorial, we deploy some of the most popular clustering and dimension reduction algorithms to perform an exploratory analysis of a dataset composed of 82 octet-binary compounds.
In this tutorial, we demonstrate how to query the NOMAD Archive from the NOMAD Analytics toolkit. We then show examples of machine learning analysis performed on the retrieved data set.
In this tutorial we will use the ElemNet neural network architecture (https://github.com/NU-CUCIS/ElemNet) to predict the volume per atom of inorganic compounds, where the open quantum materials database (OQMD) is used as a resource (specifically, the data is taken from Ward et. al., npj Comput. Mater. 2, 16028 (2016)).
Method: Neural networks
In this tutorial, we briefly introduce the main ideas behind convolutional neural networks, build a neural network model with Keras, and explain the classification decision process using attentive response maps.
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