Web apps and materials for learning about optimization (high-school level) These materials were created by Marc Brousseau, a teacher at Middleton high school in Madison, WI, working with graduate student Ben Afflerbach in the Morgan group. This work was done as part of a Research Experience for Teachers (RET) program ( at UW Madison run by the Materials Research Science and Engineering Center (MRSEC), funded under grant number DMR-1720415.


Resources for Machine Learning in Materials

  • Software
    • MAterials Simulation Toolkit – Machine Learning (MAST-ML) is an automated tool for setting up, executing, and managing output machine learning tasks in materials science.
    • MAST-ML documentation | Source (MAST-ML)
  • Educational materials
    • Modules and course plans for introductions to machine learning for materials scientists.
    • ML curriculum
  • Databases: Material’s property data sets and descriptors for easy exploration of machine learning.


StructOpt app

structOpt The StructOpt app provides a general genetic algorithm based optimizer in python targeted at identifying stable atomic structures.    

MAterials Simulation Toolkit (MAST)


  • MAST is an automated workflow manager and post-processing tool that focuses on diffusion and defect workflows using density functional theory. It interfaces primarily with the Vienna Ab-initio Simulation Package (VASP).

Download code  |  MAST manual |  Source (MAST) DOIs for MAST versions:    

Diffusion Data App

CMG_Software_diffusion Diffusion Data App is a database of over 250 (and growing) impurity diffusion coefficients calculated with ab initio density functional theory. Includes hosts Mg, Al, Cu, Ni, Pd, Pt, and W.    


mhub MaterialsHUB is an online hub of applications for computational materials science research and education related to defects and diffusion.  


atouch AtomTouch is a 3D interactive mobile platform (e.g., phone, tablet) molecular simulation software tool for educational purposes.