The genetic algorithm is a computer algorithm inspired by nature: selecting parents of a generation via some fitness function, crossing-over the parent genes (reproduction), and randomly mutating the genes of the children. In this talk, we will explore how this algorithm can be used for materials discovery by:

1. Generating swift material property prediction using machine learning (ML)
2. Creating an algorithm to design new materials from combinations of prior ones
3. Integrating the ML property predictors with the design algorithm to discover new materials

PolyGA, an implementation of the genetic algorithm for the polymer domain, will be used as the basis for this exploration.

Joe D Kern is a Materials Science and Engineering graduate student at the Georgia Institute of Technology in the Ramprasad Group. Prior to graduate school, he dual majored in Materials Science and Computer Science at the University of Wisconsin – Madison and spent six years in the Minnesota National Guard as an Arabic Linguist. His research involves utilizing computational techniques, such as genetic algorithms and machine learning, to expedite materials discovery in the polymer chemical space.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Joseph D Kern (2021), "Integrating Machine Learning with a Genetic Algorithm for Materials Exploration," https://nanohub.org/resources/35642.

  BibTex | EndNote

1. genetic algorithm
2. Jupyter notebooks
3. machine learning
4. material properties
5. materials discovery
6. NCN Group - Machine Learning Hands-on
7. PolyGA
8. polymers
9. python