In this tool, you will learn how to...
- Predict bulk modulus using machine learning 
- Use data based algorithms to sort large and complex data 
- Apply clustering, classification and regression to data in a Materials Engineering context
- Make predictions and identify trends

Matminer is a materials focused machine learning program. The program can help with...
- Retrieving data from many different data bases
- Featurizing the data so that the data are usable for machine learning 
- Creating interactive visualizations 
 

Link to Dr. Peilin Liao's faculty page

https://engineering.purdue.edu/MSE/people/ptProfile?resource_id=143902&group_id=11984

Link to Adrian Nat Gentry's Linkedin page

https://www.linkedin.com/in/adrian-nat-gentry/

Researchers should cite this work as follows:

• This notebook is adapted from "Matminer introduction - Predicting bulk modulus"

  https://github.com/hackingmaterials/matminer_examples/blob/master/matminer_examples/machine_learning-nb/bulk_modulus.ipynb

  Ward, L., Dunn, A., Faghaninia, A., Zimmermann, N. E. R., Bajaj, S., Wang, Q.,
  Montoya, J. H., Chen, J., Bystrom, K., Dylla, M., Chard, K., Asta, M., Persson,
  K., Snyder, G. J., Foster, I., Jain, A., Matminer: An open source toolkit for
  materials data mining. Comput. Mater. Sci. 152, 60-69 (2018).

• Abigail N Gentry, Peilin Liao (2020), "Introduction to Machine Learning in MSE: Predicting Bulk Modulus," https://nanohub.org/resources/msemlg. (DOI: 10.21981/4PNZ-RA03).

  BibTex | EndNote

1. bulk modulus
2. data sciences
3. educational resources
4. Jupyter notebooks
5. machine learning
6. material properties
7. materials science