Data science and machine learning are playing increasingly important roles in science and engineering, and materials science and engineering is no exception.

This online tool provides machine learning examples in the field of materials science using Jupyter notebooks, which contain step by step explanations of the activities along with live code that can be modified by 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

iii) neural network models trained to predict materials properties from basic element properties

Suggested exercises are included in each Jupyter notebook.

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, TensorFlow.

NSF Network for Computational Nanotechnology and Purdue University.

Researchers should cite this work as follows:

• Juan Carlos Verduzco Gastelum, Alejandro Strachan, Saaketh Desai (2021), "Machine Learning for Materials Science: Part 1," https://nanohub.org/resources/mseml. (DOI: 10.21981/8NFE-2F13).

  BibTex | EndNote

1. data science
2. Jupyter notebooks
3. linear regression
4. machine learning
5. materials properties
6. materials science
7. NCN Group - Machine Learning
8. NCN Group - Machine Learning Hands-on
9. NCN Group - Materials Science
10. neural networks
11. supervised learning
12. tensorflow