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Data science and machine learning are playing increasingly important role in science and engineering and materials science and engineering is not an exception. This online tool provides examples of the use of these tools in the field of materials science using Jupyter notebooks. The notebooks contain step by step explanations of the activities and live code, that can be modified by the users for hands-on learning. The initial set of tutorials focus on: i) data query, organization and visualization, ii) developing a simple model using linear regression to explore correlations between materials properties, and iii) neural network models trained to predict materials properties from basic element properties. Suggested activities are included in the Jupyter notebooks.
This tool was used in the Hands-on Machine Learning and Data Science Training Workshop conducted by nanoHUB in April 2020. Offerings for the tutorial can be found in nanoHUB resources here, here and here.
PyMatGen, Mendeleev, Keras, TensoFolow.
NSF Network for Computational Nanotechnology and Purdue University.
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