(142i) Identifying Descriptors for Dielectric Breakdown Strength Using Genetic Programming

The development of phenomenological theories by identifying relevant descriptors for material properties enables rapid screening large numbers of materials and facilitates the design of new materials. One of the leading challenges for computational researchers is determining the best ways to analyze large material data sets to identify the best descriptors for a given property. In this presentation, we demonstrate the use of genetic programming to identify relevant descriptors of dielectric breakdown based on 82 representative materials. We identified band gap E_g and phonon cut-off frequency w_max as the two most correlated features, and new classes of phenomenological models featuring exponential functions of E_g*w_max were uncovered. The genetic programming model was found to outperform other models for descriptor identification, and we discuss some of the advantages of the genetic programming approach.