Machine Learning for Materials Science: Part 1

Machine learning and data science tools applied to materials science

Launch Tool

You must login before you can run this tool.

Version 1.1 - published on 25 Feb 2019

doi:10.21981/9QJN-7N65 cite this

Open source: license | download

View All Supporting Documents

    Predicting Young's Modulus using Neural Networks Classifying crystal structures using neural networks

Category

Tools

Published on

Abstract

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.

Powered by

PyMatGen, Mendeleev, Keras, TensoFolow.

Sponsored by

NSF Network for Computational Nanotechnology and Purdue University.

Cite this work

Researchers should cite this work as follows:

  • Juan Carlos Verduzco Gastelum, Alejandro Strachan, Saaketh Desai (2019), "Machine Learning for Materials Science: Part 1," https://nanohub.org/resources/mseml. (DOI: 10.21981/9QJN-7N65).

    BibTex | EndNote

Tags