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 (https://mrsec.wisc.edu/ret/) at UW Madison run by the Materials Research Science and Engineering Center (MRSEC), funded under grant number DMR-1720415.
- Optimization of Area of a Box: https://www.geogebra.org/classic/zqeafevg
- Local Extrema with Two Independent Variables: https://www.geogebra.org/classic/waj5hm97
- Presentation: https://docs.google.com/presentation/d/1L7QNNfpbY7L6Xd3qmN9B3E-bm0-y8TsTwHHeayC06j8/edit?usp=sharing
- Handout: https://docs.google.com/document/d/1oWJQPpjzGAGIsjV0md-gpJr5bmO1M0yTHMGPjQBRYG8/edit?usp=sharing
Resources for Machine Learning in Materials
- 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.
The StructOpt app provides a general genetic algorithm based optimizer in python targeted at identifying stable atomic structures.
- 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).
MaterialsHUB is an online hub of applications for computational materials science research and education related to defects and diffusion. materialshub.org
- UW links: Project page, Online App, Source code on Github.
- IOS App – “AtomTouch” on Google play and iTunes
- Tool and educational material on BrainPOP