If we think about some important technologies which we want to improve - e.g., batteries, solar cells, sensors - we often need to invent new kinds of materials. Materials informatics is a field which incorporates "data driven" methods to understand and design new kinds of "stuff" to make new technology. In this course, we will go over a conceptual overview of applied machine learning, materials science, and predictive materials design.

Materials Informatics is a relatively new field. The central theme is using data-driven methods (vs first-principles equations / computations) to predict or understand properties of materials. In this class, we will do the following: (1) Give a very brief conceptual intro to what materials science is as a field (2) Talk about some thematic differences between 'informatics' and 'first principles' computation (3) Students will have a chance to make some cool plots (using Python) of real materials science data, which they can save and show off to their friends later!

What separates the baking-soda-and-vinegar 'volcanos' from the projects that win regional and national science fairs? We'll take a look at some case studies (and maybe use some very basic stats) to determine strategies for making a 'winning' project.