Machine learning methods are widely used as surrogate models in the physical sciences, but less explored is the use of machine learning to discover interpretable laws from data. This tutorial introduces parsimonious neural networks (PNNs), a combination of neural networks and evolutionary optimization to find models that balance accuracy with parsimony. As an example, you will learn how to train a PNN to learn interpretable laws that predict the melting temperature of a material given fundamental properties such as elastic constants and volume. You will also learn how to interpret the discovered PNN models as physical laws, and understand how various PNN models, as well as traditional models such as the Lindemann melting law, trade parsimony and accuracy.

The nanoHUB tool "Parsimonious Neural Networks" is used in this hands-on tutorial.

Please download the handout that accompanies this tutorial.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Saaketh Desai (2021), "Parsimonious Neural Networks Learn Interpretable Physical Laws," https://nanohub.org/resources/35026.

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1. hands on
2. machine learning
3. material properties
4. melting point
5. NCN Group - Machine Learning Hands-on
6. neural networks
7. parsimonious neural networks
8. tutorial